Leveraging Artificial Intelligence in Identity and Access Management

IDT-AI-101 — Student Workbook

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E1: First IAM Conversation with AI

Type: Individual + AI tool

Duration: 10 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Get hands-on with an AI chatbot immediately, establishing comfort with the tools used throughout the course.

Task:

Open your AI chatbot of choice.

Ask it: "Explain the difference between authentication and authorisation in a way that a business manager with no technical background would understand."

Review the response critically:

Is the explanation accurate?

Is the tone appropriate for the audience?

What would you change or add?

Now ask a follow-up: "Now explain Role-Based Access Control using a hospital as an example. Include why it matters for patient safety."

Compare the two responses:

How did the AI handle a general concept vs. a domain-specific scenario?

Which response was more useful? Why?

Reflect: What makes a good AI prompt? You'll build on this throughout Chapter 2.

Safety Reminder: This exercise uses only general IAM concepts. No sensitive data is involved.

Instructor-Led Addition: Have 2–3 volunteers share which response surprised them most and why.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E2: Evaluate Vendor AI Claims with AI

Type: Individual analysis with AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Develop critical evaluation skills for vendor AI marketing, using an AI chatbot as a second opinion.

Task:

Read each of the 4 anonymised vendor marketing claims below.

For each claim, make your own assessment first:

Identify which type of AI likely powers the described feature (NN for pattern recognition, LLM for language tasks, or unclear/generic).

Rate the claim 1–5 for specificity vs. vagueness:

1 = Pure "AI-washing" — no substance, just buzzwords.

3 = Some specifics but still vague about what the AI actually does.

5 = Clear, specific, credible description of the AI capability.

Paste the claim into your AI chatbot with the scene-setting prompt below and ask it to evaluate the claim.

Compare the AI's analysis to your own assessment:

Where did the AI add insight you missed?

Where did the AI lack context that you, as an IAM practitioner, would have?

Write one question you'd ask this vendor in an evaluation to cut through the marketing.

Scene-Setting Prompt (paste into your chatbot before the claims):

You are an experienced IAM architect evaluating vendor marketing claims about
AI features in identity and access management products. For each claim I share,
analyse: (1) What type of AI likely powers this feature? (2) Rate it 1-5 for
specificity — is the vendor clearly describing what the AI does, or just using
buzzwords? (3) What's missing from this claim that a buyer should ask about?
Be direct and critical. Here is the first claim:

The Claims:

Claim A: "Our platform uses advanced AI and machine learning to deliver intelligent identity security, helping you stay ahead of threats."

Claim B: "Our risk engine analyses over 50 contextual signals per authentication event, including device fingerprint, geo-velocity, IP reputation, and behavioural biometrics, to generate a real-time risk score between 0 and 100."

Claim C: "Powered by AI, our solution transforms your identity governance with next-generation intelligence."

Claim D: "Our AI copilot uses retrieval-augmented generation to query your identity data in natural language, grounding responses in your actual tenant data rather than general training data."

Discussion Points:

Which claims told you what the AI actually does vs. just saying "AI"?

Where did the AI chatbot's analysis add value? Where did it miss nuance?

What questions would help you distinguish real capability from marketing?

Instructor-Led Addition: Have participants share their most sceptical finding, the claim they rated lowest and why.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E3: Match the IAM Scenario to the AI Type

Type: Matching worksheet

Duration: 10 minutes

Goal: Apply understanding of AI learning types and underlying AI technologies to realistic IAM situations.

Task: Match each scenario (1–6) with the primary AI type (A–C) it represents:

Scenario | Primary AI Type
1. An AI system analyses years of labelled login data (known good vs. known fraudulent) to flag new potentially malicious logins. | A. Supervised Learning
2. An AI system sifts through access logs to find previously unknown groupings of users performing similar, unusual actions late at night. | B. Unsupervised Learning
3. An IAM analyst asks an AI copilot to "Summarise the access review results for the Finance department and highlight any anomalies." | C. Generative AI
4. An AI system learns baseline behaviour for each service account and flags one that suddenly starts making API calls to an unfamiliar system at 2 AM. |
5. A vendor's AI generates a detailed written report explaining why it recommended revoking a specific user's access, citing peer comparison data and usage statistics in natural language. |
6. A biometric system verifies a user's voice pattern against their stored voiceprint during a helpdesk password reset call. |

(Answers)

1 → A (Supervised — learning from labelled fraud/legitimate data to classify new attempts)

2 → B (Unsupervised — discovering unknown groupings/patterns without labels)

3 → C (Generative — LLM-powered copilot creating a summary and analysis from data)

4 → B (Unsupervised — anomaly detection based on learned baseline behaviour, no labels)

5 → C (Generative — LLM producing a natural-language explanation from structured data. Note: this is the kind of output AI copilots and IGA tools generate.)

6 → A (Supervised — neural network trained on labelled voice samples to classify match vs. no-match. Note: this uses a neural network for pattern recognition, not an LLM.)

Q1: Chapter 1 Knowledge Check

Type: Multiple-choice quiz

Duration: 10 minutes

Goal: Verify understanding of AI foundations covered in Chapter 1.

Instructions: Select the best answer for each question. Answers are provided at the end for self-scoring. Suggested passing threshold: 7/10.

1. What is the primary focus of AI in business and IAM today?

a) Creating conscious, sentient computer systems b) Replacing all human decision-making with automation c) Pattern recognition and task automation at scale d) Developing humanoid robots for physical security

2. Which AI concept is BEST described as "finding complex, non-obvious patterns in large datasets"?

a) Large Language Models b) Neural Networks c) Reinforcement Learning d) Diffusion Models

3. A UEBA system learns each user's normal login patterns and flags deviations. What type of AI learning does this primarily represent?

a) Supervised Learning b) Unsupervised Learning c) Generative AI d) Reinforcement Learning

4. An AI system is trained on thousands of login attempts already labelled as "fraudulent" or "legitimate." What type of learning is this?

a) Unsupervised Learning b) Generative AI c) Supervised Learning d) Transfer Learning

5. What is the core technical capability of Large Language Models (LLMs)?

a) Recognising faces in images b) Predicting the next token in a sequence c) Detecting network intrusions d) Rotating credentials automatically

6. Which of the following is an IAM application of Generative AI?

a) Calculating a real-time risk score for a login attempt b) Matching a selfie to an ID photo during onboarding c) Drafting an access review summary in natural language d) Detecting anomalous API call patterns from a service account

7. An IAM tool recommends revoking an entitlement because "95% of peers in the same role don't have this access." This most likely uses:

a) Generative AI for text creation b) Supervised learning from labelled compliance data c) Unsupervised learning for peer group analysis and outlier detection d) A diffusion model for image analysis

8. What makes AI agents different from traditional service accounts? (Select the BEST answer)

a) AI agents use stronger passwords b) AI agents make autonomous decisions and have dynamic access needs c) AI agents don't need authentication d) AI agents only access one system at a time

9. According to ISC2 2025, what percentage of security and identity professionals are actively pursuing AI qualifications?

a) 30% b) 50% c) 70% d) 90%

10. Which statement about AI copilots in IAM is MOST accurate?

a) They replace the need for human IAM analysts b) They use RAG to ground responses in your organisation's actual data c) They can only answer pre-defined questions from a fixed list d) They require a PhD in AI to use effectively

Q1 Answers

# | Answer | Explanation
1 | c) | AI in business focuses on pattern recognition and task automation at scale, not consciousness or replacing all human roles.
2 | b) | Neural Networks excel at finding intricate patterns in complex data, powering UEBA, biometrics, and risk scoring.
3 | b) | UEBA uses Unsupervised Learning, building baselines from unlabelled data and flagging deviations without pre-defined categories.
4 | c) | Training on data with known correct labels (fraudulent/legitimate) is the definition of Supervised Learning.
5 | b) | LLMs are fundamentally trained to predict the most likely next token in a sequence. All other capabilities emerge from this.
6 | c) | Drafting text summaries is a Generative AI (LLM) task. Risk scoring and facial matching use Neural Networks.
7 | c) | Peer group comparison and outlier detection use Unsupervised Learning to find users with access that differs from their peers.
8 | b) | AI agents make autonomous decisions and have dynamic, task-dependent access needs, unlike static service accounts.
9 | c) | ISC2 2025 Cybersecurity Workforce Study found 70% are actively pursuing AI qualifications.
10 | b) | AI copilots use Retrieval-Augmented Generation (RAG) to query your actual identity data, not just general training knowledge.

Score: ___ / 10 | Suggested passing threshold: 7/10

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E4: From Vague to Specific — Refining IAM Prompts

Type: Individual + AI tool

Duration: 10 minutes

AI Tool Required: Yes (any free-tier chatbot)

Goal: Practice transforming a poor prompt into effective, targeted prompts — and test them live.

The Poor Prompt: "Tell me something about user access."

Task:

Critique: Why is this prompt ineffective? Identify specific problems (ambiguity, lack of context, unclear goal, missing audience/format).

Rewrite: Transform it into 2–3 specific, actionable prompts for an AI assistant, targeting different potential IAM needs. Consider:

What specific information is needed?

Who is the audience / What is the desired format?

What context is necessary?

Test each prompt in your AI chatbot. Note which refinement produced the most useful output and why.

Reflect: What made the biggest difference — added context, specified format, or clearer objective?

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your test prompts.

Instructor-Led Addition: Have volunteers share their best rewrite and explain what made the difference.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E5: Hands-On — Draft a Policy Section with AI

Type: Hands-on lab with AI tool

Duration: 20 minutes

AI Tool Required: Yes (any free-tier chatbot)

Goal: Experience the iterative prompting process firsthand using a live AI tool.

Scenario: You need to draft an acceptable use policy section for MFA enrolment at your organisation.

Task:

Draft your initial prompt. Apply the principles from this chapter: be specific about the requirements, define the audience, provide context, specify format. Requirements to include in the policy section:

All employees must enrol in MFA within 30 days of account creation.

Approved MFA methods: authenticator app (preferred), hardware security key, SMS (backup only).

Employees must register at least two MFA methods.

Lost/compromised MFA devices must be reported to the helpdesk within 24 hours.

Target audience: non-technical employees.

Submit and save the first response.

Iterate at least twice. Review the output critically:

Is the tone appropriate for the audience?

Are all requirements covered?

Is anything unclear, missing, or unnecessarily complex?

Refine your prompt and resubmit. Try different techniques: add examples (few-shot), specify format changes, ask the AI to simplify language, or ask it to evaluate its own output.

Compare your first output to your final output. Note what improved and which technique made the biggest difference.

Safety Reminder: Do NOT include real company names, system names, or any sensitive information.

Discussion (5 mins): 2–3 volunteers share: What technique made the biggest difference between your first and final output?

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E6: Prompt Chaining — Security Alert Analysis

Type: Guided lab with AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot)

Goal: Practise breaking a complex analytical task into sequential AI-assisted steps.

Provided Material: Sanitised security log snippet (below — this is entirely fictional mock data, safe for any AI tool).

Sanitised Log Snippet:

Timestamp: 2025-11-14T02:47:33Z
Event: Authentication Success
User: USER_A (Role: Standard Employee, Department: Finance)
Source IP: IP_ADDR_1 (Geolocation: Country_X, City_Y)
Device: DEVICE_1 (First seen for this user)
MFA: Bypassed (exception policy POLICY_7)
Resource Accessed: FILE_SHARE_CONFIDENTIAL_FINANCE
Data Volume: 2.3 GB downloaded within 12 minutes
Previous Login: 2025-11-13T09:14:22Z from IP_ADDR_2 (Geolocation: Country_Z, City_W)
Geo-velocity: Country_X to Country_Z in 17.5 hours (flagged)
Risk Score: 87/100 (High)

Task:

Step 1 — Summarise: Paste the sanitised log into your AI tool with a prompt like: "Summarise the key information in this security alert log entry. Identify the user, action, timing, source, and any notable flags: [paste log]"

Step 2 — Identify Risks: Take the AI's summary. Paste it into a new prompt: "Based on this summary, identify 2–3 potential security risks or policy violations that an IAM team should investigate: [paste summary]"

Step 3 — Recommend Actions: Take the AI's risk assessment. Paste into a new prompt: "Given these identified risks, suggest 1–2 immediate actions an IAM administrator should take, and 1 longer-term remediation: [paste risks]"

Reflection Questions:

How did breaking the task into steps change the quality vs. asking everything at once?

At which step would human judgement be most critical?

What additional context might you add at each step to improve results?

Instructor-Led Addition: Have pairs compare results — did they get different summaries, risks, or recommendations? Why?

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E7: Build Your IAM Prompt Template

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot)

Goal: Create a reusable, safety-conscious prompt template for one common task from your actual IAM role — and test it live.

Task:

Choose one common task from your actual work that is:

Repetitive (you do it regularly)

Text-heavy (involves drafting, summarising, or explaining)

Suitable for AI assistance (a first draft would save time)

Build your template with these four components:

A. Purpose Statement

What is this template for? (One sentence)

Example: "Generate a first draft of quarterly access review summary for management reporting."

B. Required Context Fields (placeholders to fill in each time)

Example fields: [Department/Team], [Review Period], [Total Entitlements Reviewed], [Key Findings — anonymised], [Audience]

C. Safety Checklist (verify before every use)

☐ No real usernames, email addresses, or employee IDs

☐ No real system names, IP addresses, or URLs

☐ No credentials, API keys, or secrets

☐ No specific incident details or non-public findings

☐ Output will be reviewed by a human before use

D. Example Completed Prompt

Show what the template looks like when all fields are filled in with safe, anonymised content.

Test your template: Fill in the placeholders with anonymised content and submit to your AI chatbot. Does the output match what you need? Refine the template based on what comes back.

Bonus: Convert to AI Project (if time permits): Take your working template and set it up as system instructions in a Claude Project, Custom GPT, or Gemini Gem. Start a new conversation in that project and notice how the pre-configured context improves response quality without repeating your setup each time.

Example Template (Illustrative):

Purpose: Draft a user communication about an upcoming password policy change.

Context Fields: [Policy Change Summary], [Effective Date], [User Action Required], [Support Contact — generic], [Tone: formal/friendly]

Safety Checklist: [as above]

Example Prompt: "Draft a friendly email to all employees announcing that our password policy is changing. Key changes: minimum length increasing from 8 to 12 characters, effective [DATE]. Users must update their password before the deadline. Include a link to [GENERIC SUPPORT PAGE] for help. Tone: friendly and encouraging, not threatening. Keep it under 200 words."

Takeaway: Keep your template. Refine it as you use it. Share useful templates with your team.

Instructor-Led Addition: Have 2–3 students share their templates to inspire others.

Q2: Chapter 2 Knowledge Check

Type: Multiple-choice quiz

Duration: 10 minutes

Goal: Verify understanding of AI interaction skills, context engineering, and safe AI usage covered in Chapter 2.

Instructions: Select the best answer for each question. Answers are provided at the end for self-scoring. Suggested passing threshold: 7/10.

1. What is the "Golden Rule" for using AI tools in IAM?

a) Always use the most expensive AI model available b) Never input sensitive or confidential data into public AI tools c) Only use AI tools approved by your vendor d) Always accept the AI's first output without iteration

2. Which of the following data types should NEVER be entered into a public AI tool?

a) General definitions of IAM concepts b) Published regulatory requirements c) Internal system names, IP addresses, and credentials d) Anonymised, fictional scenario descriptions

3. In "context engineering," what does the AI's context include beyond just your prompt?

a) Only the AI model's training data b) System instructions, retrieved data (RAG), tool definitions, and conversation history c) The AI model's temperature and token limit settings d) Other users' previous conversations with the same AI

4. A prompt includes two example pairs of "technical finding → user notification" before asking the AI to generate a new notification. This is an example of:

a) Zero-shot prompting b) Few-shot prompting c) Prompt chaining d) Meta-prompting

5. What is the main advantage of prompt chaining?

a) It makes prompts shorter and simpler b) It breaks complex tasks into manageable steps with human review at each stage c) It eliminates the need for human review of AI output d) It only works with paid AI tools

6. What does RAG (Retrieval-Augmented Generation) do?

a) Generates completely new training data for AI models b) Retrieves relevant data from your knowledge base and feeds it to the LLM alongside the query c) Automatically rotates AI model credentials d) Blocks sensitive data from reaching public AI tools

7. Which common prompt pitfall involves asking the AI a question like "Summarise the latest security incident" without providing any incident details?

a) Ambiguity b) Overly complex question c) Implicit assumptions (expecting AI to know your internal context) d) Not specifying format

8. You need AI help with a task involving sensitive access review data. What is the correct approach?

a) Paste the raw data into ChatGPT for quick analysis b) Anonymise all identifiers (users → USER_A, systems → SYSTEM_1) before using any public AI tool c) Assume the data is safe because the AI deletes it after processing d) Only use AI tools on weekends when less monitoring occurs

9. When should you use an AI thinking/reasoning mode (e.g., extended thinking, deep reasoning) for IAM tasks?

a) For every prompt, to ensure the highest-quality answer every time b) For complex analysis involving multiple regulations, risk factors, or competing requirements c) Only for generating written documents longer than 1,000 words d) Never -- standard prompting is always sufficient for professional work

10. When building a reusable IAM prompt template, which component is MOST important for preventing data leaks?

a) The purpose statement b) The required context fields c) The safety checklist (verify no sensitive data before each use) d) The example completed prompt

Q2 Answers

# | Answer | Explanation
1 | b) | The golden rule: never input sensitive or confidential data into public AI tools. Established in slide 2.4.
2 | c) | Internal system names, IPs, and credentials are sensitive data that must never enter public AI tools.
3 | b) | Context engineering recognises that AI output depends on the full information environment: system instructions, retrieved data (RAG), tool definitions, conversation history, and the prompt itself.
4 | b) | Providing example input-output pairs to guide the AI is few-shot prompting.
5 | b) | Chaining breaks complex tasks into sequential steps, enabling human review and correction at each stage.
6 | b) | RAG retrieves relevant data from your organisation's knowledge base and feeds it to the LLM alongside the user's query, grounding responses in your actual data.
7 | c) | Expecting the AI to know about your internal incidents without providing context is making implicit assumptions.
8 | b) | Always anonymise all identifiers before using public AI tools. The safe workflow: extract → anonymise → prompt → verify.
9 | b) | Thinking/reasoning modes are most valuable for complex analytical tasks (multi-regulation compliance, risk assessment with many variables). They use more compute and are slower, so they're unnecessary for simple lookups or routine drafting.
10 | c) | The safety checklist ensures no sensitive data (PII, system names, credentials) is included before each use.

Score: ___ / 10 | Suggested passing threshold: 7/10

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E8: AI Copilot Query Design

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Experience the copilot query design process. Understand which questions AI copilots answer well (with RAG and real data) and which they struggle with, reinforcing why data grounding matters.

Scenario: You are the IAM administrator at NordTech Solutions, a fictional technology company (2,000 employees, Microsoft 365, Entra ID for identity, SailPoint for IGA). Your organisation is evaluating an AI copilot for the IAM team. You want to understand what kinds of questions a copilot could realistically answer.

Task:

Step 1 — Design Your Queries (5 min): Write 5 natural-language queries you would want to ask an AI copilot about your identity environment. Aim for a mix:

At least 1 query about user access (e.g., "Who has admin access to the finance SharePoint site?")

At least 1 query about policy or compliance (e.g., "Which users haven't completed their access review this quarter?")

At least 1 query about anomalies or risk (e.g., "Show me accounts with impossible travel events in the last 7 days")

For each query, predict: Would this work well with RAG (grounded in your real tenant data) or would the copilot struggle without data access?

# | Your Query | Prediction: Works with RAG?
1 |  | Yes / No / Partially
2 |  |
3 |  |
4 |  |
5 |  |

Step 2 — Test with Your AI Chatbot (5 min): Paste the following scene-setting prompt into your AI chatbot, then submit your 5 queries one at a time:

You are an AI copilot for an IAM team at a fictional company called NordTech
Solutions (2,000 employees, Microsoft 365, Entra ID, SailPoint for IGA).
You do NOT have access to any real identity data. I will ask you questions
that an IAM copilot might receive. For each question, respond in two parts:
(1) Your best attempt at answering based on general IAM knowledge,
(2) A brief note explaining what specific data you would need (from Entra ID,
SailPoint, SIEM logs, etc.) to give an accurate, grounded answer. Be honest
about what you can and cannot answer without real data.

Step 3 — Evaluate and Reflect (5 min):

Which queries did the chatbot handle well even without real data? Why?

Which queries were clearly useless without tenant-specific data?

Did any responses sound authoritative but were actually generic? (This is a key copilot risk.)

What does this tell you about the difference between a general chatbot and a RAG-powered copilot?

Key Insight: A copilot without RAG is just a chatbot with IAM vocabulary. The value of enterprise copilots (Security Copilot, Harbor Pilot) comes from grounding responses in your actual data. This exercise demonstrates why data readiness (the overpermissioning problem from Section 3.2) matters before copilot deployment.

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts. Use the fictional NordTech scenario.

Instructor-Led Addition: Have 2–3 participants share the query where the gap between "general AI answer" and "RAG-grounded answer" was most obvious. Use this to reinforce the RAG concept from Section 3.2.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E9: Simulating Risk-Based Policy Decisions with AI

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Understand how AI risk scores drive adaptive authentication policies, compare your own judgement to an AI risk engine's output, and surface the gap around non-human identities.

Scenario: Your organisation uses an AI risk engine and has the following Conditional Access policy:

IF Risk Score = Low (0–30) → Allow Access (No MFA)

IF Risk Score = Medium (31–70) → Require MFA

IF Risk Score = High (71–100) → Block Access & Alert Security

Task:

Step 1 — Make Your Own Decisions (5 min): Review each login attempt below. Based on your IAM experience, decide what the policy outcome should be:

# | Identity | Context | Your Decision?
1 | Alice (Human) | Known device, usual location, business hours | ?
2 | Bob (Human) | New device, unusual location (valid travel) | ?
3 | Charlie (Human) | Login attempts from known malicious IP | ?
4 | David (Human) | Known device, usual location, impossible travel detected | ?
5 | Eva (AI Agent) | Procurement bot making API call to purchasing system at 3:00 AM, automated order processing | ?

Step 2 — Ask the AI (5 min): Paste the following scene-setting prompt into your AI chatbot, followed by the five scenarios:

You are a risk-based authentication engine for a global financial services
company. You evaluate login attempts and assign risk scores from 0–100.
Your policy thresholds are: Low (0–30) = Allow Access (No MFA),
Medium (31–70) = Require MFA, High (71–100) = Block Access & Alert
Security. I will give you login scenarios. For each, provide: (1) your
risk score, (2) your access decision, and (3) the top 3 factors that
influenced your assessment. Here are the scenarios:

Scenario 1: Alice (Human) — Known corporate device, usual office location,
login at 10:15 AM on a Tuesday.

Scenario 2: Bob (Human) — New device never seen before, login from a city
1,500 km from his usual location, 2:30 PM local time. HR records confirm
he is on an approved business trip.

Scenario 3: Charlie (Human) — Login attempt from an IP address flagged in
3 threat intelligence feeds as associated with credential stuffing attacks.
Device fingerprint is unknown.

Scenario 4: David (Human) — Known corporate device, usual office location,
but a successful login was recorded from a different country 45 minutes ago
(impossible travel detected).

Scenario 5: Eva (AI Agent) — A procurement automation bot making an API call
to the corporate purchasing system at 3:00 AM. The bot is processing a batch
of routine purchase orders. It authenticates using a service account.

Step 3 — Compare and Reflect (5 min):

Compare the AI's decisions to your own. Where do they agree? Where do they differ?

Did the AI identify risk factors you overlooked?

Where does the AI lack context that you, as an IAM practitioner, would have?

Pay special attention to Scenario 5: how did the AI handle a non-human identity? Did the chatbot handle non-human identity correctly?

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts.

Answers (Scenarios 1–4):

# | Identity | Policy Outcome
1 | Alice | Allow Access (No MFA) — trusted context, low risk
2 | Bob | Require MFA — elevated risk, but legitimate travel is possible; MFA verifies identity
3 | Charlie | Block Access & Alert Security — known malicious source
4 | David | Block Access & Alert Security — impossible travel is a strong credential compromise indicator

Answer (Scenario 5 — Discussion):

Eva's scenario is deliberately designed to surface gaps:

Does your current adaptive auth policy even cover non-human identities?

Most risk engines and Conditional Access policies were designed for human login patterns: 3:00 AM activity is "anomalous" for humans but normal for bots

Does Eva have her own identity, or is she using a shared service account?

Who is accountable if Eva's credentials are compromised?

What risk signals even apply to an AI agent? (Device? Location? Behaviour?)

Key insight: The policies we've built for human identities often don't translate to AI agents. This gap is what Section 3.3 addresses.

Note how the AI chatbot responded: Most chatbots will either apply human-centric risk logic to Eva (flagging 3:00 AM as suspicious) or acknowledge uncertainty. Both responses reveal the same gap: current risk frameworks were not designed for non-human identities.

Instructor-Led Addition: Have volunteers share where their assessment differed most from the AI's. Use Eva's scenario to bridge into the NHI/agent identity discussion.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E10: The Oversharing Audit with AI Analysis

Type: Individual + AI tool

Duration: 20 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Understand how AI copilot deployment amplifies the overpermissioning problem, use an AI chatbot to identify risks, and critically evaluate its analysis.

Scenario: You are the IAM team at TechGlobal, a 5,000-employee company. Here is your environment:

Platform: Microsoft 365 (Exchange, SharePoint, Teams, OneDrive) across all employees

SharePoint: 2,400+ sites accumulated over 8 years. Permissions are inconsistent:

Many sites use "Everyone except external users" sharing

Department sites often have members from other departments who were added ad-hoc and never removed

Several legacy sites contain M&A due diligence materials from 3 years ago, still accessible to the original project team (40+ people across departments)

Groups: 850+ Microsoft 365 groups and security groups. Many have no documented owner. Membership reviews haven't been done consistently.

OneDrive: No consistent sharing policies. Some employees share folders broadly for convenience.

Sensitivity Labels: Deployed but adoption is uneven, fewer than 30% of documents are labelled

Plan: Executive leadership wants to deploy Microsoft Copilot for M365 across all employees next quarter to "boost productivity"

Task:

Step 1 — Read the Scenario (3 min): Read the TechGlobal environment description above carefully. Before using the AI, write down the 3 oversharing risks you consider most dangerous. Note why each is critical.

Step 2 — AI Analysis (2 min): Paste the following scene-setting prompt into your AI chatbot, followed by the scenario details:

You are a Microsoft 365 security consultant specialising in data governance
and AI readiness. I'm going to describe a fictional organisation's
environment. Analyse the environment and: (1) Identify the 5 highest-risk
oversharing scenarios if Microsoft Copilot for M365 is deployed across all
employees, (2) For each risk, explain what content is at risk, who could
access it via Copilot who shouldn't, and the potential business impact,
(3) Recommend 3 specific, actionable pre-deployment governance requirements.
Be concrete — not vague principles. Here is the organisation profile:

Organisation: TechGlobal, 5,000 employees
Platform: Microsoft 365 (Exchange, SharePoint, Teams, OneDrive) — all employees
SharePoint: 2,400+ sites over 8 years, permissions inconsistent. Many sites use
"Everyone except external users" sharing. Department sites have ad-hoc members
from other departments never removed. Legacy sites contain M&A due diligence
from 3 years ago — still accessible to original project team (40+ people across
departments).
Groups: 850+ M365 and security groups, many without documented owners.
Membership reviews not done consistently.
OneDrive: No consistent sharing policies, some employees share folders broadly.
Sensitivity Labels: Deployed but under 30% adoption.
Plan: Deploy Microsoft Copilot for M365 to all employees next quarter.

Step 3 — Critically Evaluate the AI's Analysis (10 min):

What risks did it identify correctly?

What did it miss?

Were its governance requirements specific enough?

Are its example queries realistic? Would employees actually ask those questions?

Did it consider regulatory implications (GDPR, industry-specific)?

What assumptions did it make that may not apply to your real environment?

Step 4 — Draft Your Final Governance Requirements (5 min): Combining the AI's analysis with your own judgement, draft 3 specific, actionable pre-deployment governance requirements that the IAM team should insist on before Copilot goes live. These must be concrete, not vague principles like "improve security." Use this format: "Before Copilot deployment, [specific action] must be completed for [specific scope]."

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts.

Example Risks (for instructor reference):

M&A due diligence materials accessible to 40+ people. Copilot query "What acquisitions is TechGlobal considering?" could surface confidential deal terms

HR sites with performance reviews, salary data, PIP documentation shared too broadly. Any employee could ask "What is [colleague]'s salary?"

Executive strategy documents in SharePoint with "Everyone" sharing. Competitive intelligence accessible company-wide

Customer contracts with pricing data in broadly shared project sites. Employees outside sales could discover pricing strategies

Legal hold materials from past litigation still accessible to original case team members who've moved roles

Example Governance Requirements (for instructor reference):

"Before Copilot deployment, all SharePoint sites containing HR, legal, financial, or M&A content must have permissions audited and restricted to named individuals or role-based groups, no 'Everyone' sharing."

"Before Copilot deployment, all Microsoft 365 groups must have a documented owner, and an access review must be completed for all groups with more than 50 members."

"Copilot will be deployed in phases by department, starting with lowest-sensitivity teams, with a 30-day monitoring period before expanding to departments with access to sensitive content."

Instructor-Led Addition: Have participants present their strongest governance requirement and their most insightful critique of the AI's analysis. Did different AI tools produce different risk rankings?

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E11: Design an AI Agent Identity Policy with AI

Type: Individual + AI tool

Duration: 25 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Apply IAM governance principles to the emerging challenge of AI agent identity management, using an AI chatbot as a drafting partner and then critically improving its output.

Scenario: You are the IAM team at EuroManufacture GmbH, a mid-sized manufacturing company (1,200 employees, 3 EU locations). The procurement department wants to deploy an AI agent called "ProcureBot" to automate routine purchasing tasks.

ProcureBot's Intended Capabilities:

Browse approved supplier catalogues (3 pre-approved suppliers)

Compare prices across suppliers for requested items

Generate purchase orders for standard office and manufacturing supplies

Submit orders up to €10,000 autonomously

Flag orders above €10,000 for human approval

Track order delivery status and update the internal procurement system

Send notifications to the requesting employee when orders are placed or delivered

ProcureBot's Technical Environment:

Connects to 3 external supplier APIs

Reads from and writes to the internal SAP procurement module

Sends notifications via the internal Microsoft Teams instance

Runs on Azure infrastructure as a containerised service

Developed by an external vendor; maintained by internal IT operations

Task:

Step 1 — Read the ProcureBot Scenario (3 min): Review the agent specification above carefully. Note the systems involved, the capabilities, and the trust boundaries.

Step 2 — Get a First Draft from AI (5 min): Paste the following scene-setting prompt into your AI chatbot:

You are an IAM security architect specialising in non-human identity
governance. I need to design a complete identity policy for an AI
procurement agent. I'll describe the agent's capabilities and environment.
Generate a comprehensive identity policy covering: (1) Authentication —
how the agent authenticates to each system, credential types, rotation,
compromise response, (2) Permissions — least-privilege access for each
system, hard boundaries, (3) Human approval thresholds — transaction
amounts, action types, approver roles, timeout handling, (4) Audit &
monitoring — logging, anomaly detection, SIEM integration, (5) Lifecycle —
ownership, review cadence, decommissioning procedures. Be specific — vague
statements like "use strong authentication" are not sufficient. Here is the
agent specification:

Agent Name: ProcureBot
Organisation: EuroManufacture GmbH (1,200 employees, 3 EU locations,
manufacturing sector)
Purpose: Automate routine procurement of office and manufacturing supplies

Capabilities:
- Browse 3 pre-approved supplier catalogues via API
- Compare prices across suppliers
- Generate and submit purchase orders for standard supplies
- Submit orders up to EUR 10,000 autonomously
- Flag orders above EUR 10,000 for human approval
- Track delivery status and update internal procurement system
- Notify requesting employees via Microsoft Teams

Technical environment:
- Connects to 3 external supplier APIs
- Reads/writes to internal SAP procurement module
- Sends notifications via Microsoft Teams
- Runs on Azure as a containerised service
- Developed by external vendor, maintained by internal IT operations

Step 3 — Critically Review the AI's Draft Policy (10 min): Read the AI's draft carefully. Use the checklist below to identify gaps. For each gap you find, add or revise the relevant policy section. Your goal is a policy that would survive scrutiny from a security auditor.

Common Gaps to Check For:

Does the policy set a daily/weekly aggregate spending cap? (A compromised bot could place thousands of €9,999 orders)

Does ProcureBot use separate credentials per connected system? (Single credential compromise should not grant access to everything)

Is there an incident response procedure specific to ProcureBot compromise?

Are operating hours restricted? (Should ProcureBot run 24/7, or only during business hours?)

Is there version control or change management for ProcureBot's code and configuration?

Is audit log retention aligned with regulatory requirements? (Varies by EU member state)

What happens if the human owner/sponsor leaves the company?

Is there a testing/staging environment policy, or do changes go straight to production?

Step 4 — Produce Your Final Policy (7 min): Write the final version of your policy, incorporating the AI's useful contributions and your own improvements. Note which additions came from your own expertise versus the AI's draft.

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts.

Instructor-Led Addition: Have participants share the most significant gap they found in the AI's draft. Optionally, have pairs swap policies and conduct a peer critique: one thing done well, one significant gap, one requirement they would make stricter.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E12: Shadow AI Discovery Challenge with AI

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Surface the breadth of shadow AI vectors in a typical organisation, use AI to expand your thinking, and position IAM controls as the primary line of defence.

Task:

Step 1 — Solo Brainstorm (5 min): Working individually, brainstorm all the ways employees might use unauthorised AI tools at work. For each method, document three things:

Column | What to Document
Tool / Method | Name the specific AI tool or access method
Data Leak Risk | What types of sensitive data could be exposed?
IAM Control | What specific IAM control could detect or prevent this?

An entry is valid if:

The tool/method is realistic (it exists and employees could plausibly use it)

The data leak risk is plausible for that tool

The IAM control is specific — "better training" or "awareness" do NOT count as IAM controls

Try to identify as many valid entries as you can on your own before proceeding to Step 2.

Step 2 — AI Expansion (5 min): Paste the following scene-setting prompt into your AI chatbot, including your list from Step 1:

You are a cybersecurity consultant specialising in shadow IT and data loss
prevention. I've brainstormed a list of ways employees might use
unauthorised AI tools at work. For each method I've identified, I've noted
the data leak risk and a specific IAM control. Review my list, then:
(1) Identify any shadow AI methods I missed — aim for at least 5 additional
methods, (2) For each new method, provide the same three columns: tool/method,
data leak risk, and a specific IAM control (not just "awareness training"),
(3) Are any of my proposed IAM controls insufficient? Suggest improvements.
Here is my list:

[PASTE YOUR LIST FROM STEP 1 HERE]

Step 3 — Evaluate and Finalise (5 min):

Review the AI's suggestions critically — are they realistic? Are the IAM controls actually actionable?

Add valid entries to your table. Discard any that are unrealistic or have vague controls.

Target: 15+ total valid methods across both your original list and AI-assisted additions.

Scoring (for classroom use):

Each valid entry = 1 point

Bonus point for any entry no other participant identified

Invalid entries (unrealistic tool, vague control) = 0 points

Example Entries (for instructor reference — do NOT share with participants upfront):

# | Tool / Method | Data Leak Risk | IAM Control
1 | ChatGPT via web browser | Source code with embedded API keys, customer PII, internal policies | CASB blocking unapproved AI domains; URL filtering
2 | Gemini/Claude via personal mobile device on corporate Wi-Fi | Email content copy-pasted, meeting notes, strategy documents | Network monitoring for AI service endpoints; MDM policy requiring managed devices for corporate data
3 | GitHub Copilot (free tier, personal account) in VS Code | Proprietary source code, database schemas, internal API structures | Endpoint DLP detecting code uploads; software inventory control blocking unapproved extensions
4 | AI features built into Notion, Slack, or other approved SaaS tools | Data already in the SaaS tool sent to AI sub-processors | Vendor contract review; SaaS access governance; CASB monitoring of AI feature usage within SaaS apps
5 | ChatGPT API called from personal scripts on corporate laptop | Any data the script processes: logs, configs, credentials | Endpoint monitoring for API calls to AI endpoints; egress filtering; application allowlisting
6 | AI-powered browser extensions (grammar, writing, summarisation) | Email content, document text, form data from any web page | Browser extension governance policy; endpoint management blocking unapproved extensions
7 | Uploading documents to AI-powered transcription/translation services | Meeting recordings, internal documents, legal materials | DLP on file upload to unapproved cloud services; CASB
8 | AI features auto-enabled in Microsoft 365 / Google Workspace | Content from email, documents, calendars | Tenant-level AI feature configuration; admin controls for AI opt-in/opt-out per user group
9 | Personal ChatGPT Plus account on personal device, copy-pasting from corporate apps | Any content visible on screen: email, documents, dashboards | Conditional Access blocking corporate app access on unmanaged devices; DLP on clipboard/copy actions
10 | AI-powered email assistants (auto-reply, summarise) via third-party apps with OAuth access to mailbox | Full email content, attachments, contact lists | OAuth app consent policies; app governance reviewing third-party OAuth grants; periodic app access reviews
11 | Voice-to-AI via mobile assistant (Siri, Google Assistant) in meetings | Spoken confidential information, meeting content | Physical security awareness (not IAM); meeting recording policies; device management policies
12 | AI image generation tools for presentations using confidential diagrams/screenshots as input | Architecture diagrams, UI screenshots, internal process flows | DLP for image upload; CASB monitoring

Post-Exercise Reflection:

How many methods did you find on your own vs. with AI assistance?

Did the AI suggest any vectors that surprised you?

Which IAM controls from the combined list are you already using? Which are gaps?

Note the pattern: many shadow AI vectors involve OAuth consent (entry 10), browser extensions (entry 6), or SaaS-embedded AI features (entries 4, 8), all things IAM teams should be governing but often aren't

The key insight: shadow AI governance isn't a single control. It requires a layered approach combining access management, endpoint management, network controls, and data protection, all coordinated through IAM governance

Instructor-Led Addition: Run a competitive scoring round: who found the most valid methods? Award a bonus point for any unique method no one else identified.

Q3: Chapter 3 Knowledge Check

Type: Multiple-choice quiz

Duration: 10 minutes

Goal: Verify understanding of AI in IAM platforms, copilots, NHI governance, and shadow AI covered in Chapter 3.

Instructions: Select the best answer for each question. Answers are provided at the end for self-scoring. Suggested passing threshold: 7/10.

1. What is the primary function of UEBA (User and Entity Behaviour Analytics) in IAM?

a) Generating user access reports b) Learning baseline behaviour patterns and flagging anomalies c) Automating password resets d) Managing multi-factor authentication tokens

2. In risk-based adaptive authentication, what happens when a login attempt receives a "High" risk score?

a) The user is prompted to change their password b) Access is allowed with additional logging c) Access is blocked and security is alerted d) The user is asked to verify their email address

3. What is the "overpermissioning problem" that must be addressed before deploying AI copilots?

a) AI copilots require too many API permissions to function b) Users have excessive access to data that copilots can then surface and expose c) AI copilots automatically grant additional permissions to users d) Copilot licensing is too expensive for most organisations

4. What technology allows AI copilots to answer questions about YOUR organisation's data rather than relying solely on general training data?

a) Transfer learning b) Reinforcement learning from human feedback c) Retrieval-Augmented Generation (RAG) d) Federated learning

5. Approximately what is the ratio of non-human identities (NHIs) to human identities in a typical enterprise?

a) 1:1 b) 5:1 c) 20:1 d) 50:1 or higher

6. What makes AI agents fundamentally different from traditional service accounts?

a) AI agents use stronger encryption b) AI agents make autonomous decisions and have dynamic, task-dependent access needs c) AI agents don't require any authentication d) AI agents can only access one system at a time

7. What percentage of employees are estimated to use unapproved AI tools at work (shadow AI)?

a) Less than 20% b) About 40% c) About 60% d) Over 80%

8. Which of the following is the MOST effective IAM control for governing shadow AI usage?

a) Annual security awareness training b) Verbal policy reminders in team meetings c) CASB blocking of unapproved AI domains combined with SSO enforcement d) Trusting employees to follow the acceptable use policy

9. When budgeting for AI in IAM, what does the "total cost of ownership" (TCO) include beyond licensing fees?

a) Only the subscription cost and implementation b) Licensing + integration + data readiness + ongoing operations + governance c) Only the cost of the AI model itself d) Just the vendor contract and annual renewal

10. Why should organisations define human approval thresholds for AI agents rather than allowing fully autonomous operation?

a) AI agents are too slow to make decisions on their own b) Regulations prohibit any autonomous AI decisions c) A compromised or malfunctioning agent could take damaging actions at scale without human detection d) Human approval makes AI agents faster and more efficient

Q3 Answers

# | Answer | Explanation
1 | b) | UEBA learns normal behaviour baselines and flags deviations, the core of anomaly detection in IAM.
2 | c) | High risk scores typically trigger access blocking and security alerts as the most protective response.
3 | b) | Copilots inherit the user's existing permissions. If users have excessive access, the copilot exposes data they shouldn't see. 95% of permissions go unused.
4 | c) | RAG retrieves relevant data from your knowledge base and feeds it to the LLM alongside the query, grounding responses in your actual data.
5 | d) | Research indicates NHIs outnumber human identities by 50:1 or more in typical enterprises.
6 | b) | Unlike static service accounts, AI agents make autonomous decisions and their access needs change dynamically based on the task.
7 | d) | Research shows over 80% of employees use unapproved AI tools, making shadow AI a pervasive governance challenge.
8 | c) | Technical controls (CASB, SSO enforcement, URL filtering) are far more effective than awareness-only approaches for preventing unauthorised AI tool usage.
9 | b) | TCO encompasses the full lifecycle: licensing, integration, data readiness (e.g., fixing overpermissioning), operations, governance, and hidden costs like vendor lock-in.
10 | c) | Without human approval thresholds, a compromised agent could execute thousands of harmful actions (e.g., unauthorised purchases) before detection.

Score: ___ / 10 | Suggested passing threshold: 7/10

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E13: Deepfake Detection Challenge

Type: Interactive demo

Duration: 15 minutes

Goal: Experience first-hand how difficult it is to detect AI-generated content and discuss the implications for IAM.

Setup

The instructor presents 5 media clips (video, audio, and/or images). For each clip, participants vote: Real or Deepfake?

TODO: Source concrete media resources for this exercise. Candidates include:

MIT Media Lab deepfake demonstration videos

Academic deepfake detection challenge datasets (e.g., FaceForensics++, Deepfake Detection Challenge)

News coverage with side-by-side real vs. deepfake comparisons

Publicly available deepfake detection tools with sample content (e.g., Microsoft Video Authenticator demos, Sensity AI examples)

For self-paced delivery: curate 5 specific URLs or embed media files that participants can review independently

The Clips

# | Type | Description | Vote
1 | Video | Executive delivering a quarterly update | Real / Deepfake?
2 | Audio | Phone call requesting an urgent wire transfer | Real / Deepfake?
3 | Image | Passport-style headshot for KYC verification | Real / Deepfake?
4 | Video | Remote job interview candidate introducing themselves | Real / Deepfake?
5 | Audio | Voicemail from IT support requesting credential verification | Real / Deepfake?

Reveal & Discussion

After all votes are collected, reveal the answers.

Key statistic: Research shows humans detect high-quality deepfakes at only 24.5% accuracy — worse than random chance.

Discussion Questions (10 minutes, group)

How did you do? Were you surprised by any results? What made certain clips convincing or suspicious?

IDV Implications: If trained professionals struggle to detect deepfakes, what does this mean for:

Document-based identity verification during remote onboarding?

Video-based identity proofing for high-value accounts?

Liveness detection — is it still reliable against AI-generated video?

Helpdesk & Approval Procedures: Consider the Arup incident ($25.6M deepfake video call). How should organisations update:

Callback verification procedures (if voice can be cloned)?

Approval workflows for financial transactions?

Policies for video-based identity confirmation?

What would have saved them? In the Ferrari case, an executive asked a personal question the AI couldn't answer. What other verification methods could work when audio and video are no longer trustworthy?

Key Takeaway: Human detection alone is no longer sufficient. IAM controls must account for the reality that voice, video, and images can be convincingly fabricated. Multi-factor, multi-channel verification with out-of-band confirmation becomes essential.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E14: Responsible AI Scenarios with AI Analysis

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Apply responsible AI principles (bias, privacy, compliance, oversight) to realistic IAM scenarios — and critically evaluate an AI chatbot's analysis of the same scenarios.

Task

Read each scenario carefully and form your own initial assessment of the responsible AI issues, applicable regulations, and recommended actions. (3 min)

For each scenario, paste it into your AI chatbot using the scene-setting prompt below. Review the AI's analysis. (5 min)

Critically evaluate the AI's analysis: (5 min)

Did the AI identify the right regulations for each jurisdiction?

Did it miss any cultural or regional context?

What would you add or change?

Write your final assessment for each scenario combining your analysis with the AI's insights — noting where the AI added value and where it fell short. (2 min)

Reflection: Where did the AI's regulatory knowledge add value? Where did it lack the nuance of an IAM practitioner's judgement?

Scene-Setting Prompt (paste into your chatbot before each scenario)

You are a responsible AI consultant specialising in identity and access management.
I will share scenarios involving AI-powered IAM systems that raise ethical, legal, and
governance concerns. For each scenario, analyse: (1) The primary responsible AI issues
(bias, privacy, fairness, explainability, compliance risk), (2) The specific regulations
or frameworks that may be violated — be precise about jurisdictions, (3) The potential
consequences for the organisation, (4) What should have been done differently. Be
thorough and cite specific regulatory articles where possible. Here is the first scenario:

Scenario A: The European Bank Lockouts

A major European bank deploys an AI-powered adaptive authentication system across its online banking platform serving customers in 40+ countries. The risk engine uses login location as a significant factor. Within three months, customer complaints reveal that users logging in from several African countries experience 5x higher MFA challenge rates and 3x more account lockouts than users in Western Europe — despite identical account histories and no evidence of increased fraud from those regions.

The bank's IAM team investigates and discovers the risk model was trained primarily on European login data and treats unfamiliar geographic patterns as inherently suspicious. No bias testing across geographic regions was conducted before deployment.

After reviewing the AI's analysis, consider:

Did the AI correctly identify EU AI Act risk classification for this system?

Did it cite the specific GDPR articles relevant to automated decision-making?

Did it address the financial services regulatory angle?

What regional nuances might the AI have missed?

Scenario B: The Singapore Data Upload

An IAM team at a Singapore-based financial services company is overwhelmed with access certification reviews. A team member discovers they can upload their access review spreadsheet — containing employee names, departments, job titles, system access lists, and manager names — to a free AI summarisation tool to generate review recommendations.

The team member uploads 6 months of access review data covering 2,000 employees to the free tool. They share the resulting AI-generated recommendations with their manager, who is impressed by the efficiency gain and suggests the whole team adopt this approach.

After reviewing the AI's analysis, consider:

Did the AI correctly identify Singapore PDPA provisions (not just GDPR)?

Did it mention the 2024 PDPA amendment penalties?

Did it address the cross-border data transfer implications?

Did it consider the financial services regulatory context specific to Singapore (MAS guidelines)?

Scenario C: The Australian Airport Biometrics

An Australian airport deploys an AI-powered facial recognition system at immigration gates to speed up passenger processing. After six months of operation, data analysis reveals that passengers from South Asian and Southeast Asian backgrounds experience a false rejection rate 12x higher than passengers of European descent. These passengers must queue for manual processing, adding 20–40 minutes to their transit time.

The airport authority was aware of NIST's bias research but believed their vendor's claims that the system had been "tested for fairness." No independent bias audit was conducted. The system's vendor is a company previously fined by data protection authorities in multiple countries for privacy violations related to facial data collection.

After reviewing the AI's analysis, consider:

Did the AI reference the specific Australian Privacy Act provisions?

Did it mention the Clearview AI enforcement precedent in Australia?

Did it address the vendor due diligence failure adequately?

Did it consider international human rights frameworks and discrimination law?

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts.

Instructor-Led Addition: After individual work, invite 2–3 participants to share where the AI's analysis surprised them — either by adding something valuable or by missing something obvious.

Discussion Guide (For Instructor)

Scenario A Key Points:

Issues: Algorithmic bias (geographic/demographic proxy), lack of pre-deployment bias testing, no disaggregated performance metrics, potential indirect discrimination

Regulations: EU AI Act (risk scoring likely high-risk under Annex III), GDPR Article 22 (automated decision-making), GDPR non-discrimination principles, financial services conduct regulations

Consequences: Regulatory fines (EU AI Act: up to 35M EUR / 7% global turnover), GDPR enforcement, customer attrition, reputational damage, potential class-action litigation

Better approach: Pre-deployment bias testing across all served geographies, disaggregated performance metrics, fairness thresholds, appeal mechanism for affected customers, ongoing monitoring

Scenario B Key Points:

Issues: Massive data privacy violation (PII to unvetted public tool), no data classification awareness, no approval process, shadow AI use

Regulations: Singapore PDPA (consent, purpose limitation, protection obligation, transfer limitation), financial services data handling requirements, likely violates internal data classification and acceptable use policies

Consequences: PDPA fines (up to SGD 1M or 10% annual turnover under 2024 amendments), regulatory investigation, potential data breach notification obligations, disciplinary action, reputational damage

Better approach: Use organisation's approved AI tools with enterprise data protections, anonymise data before any external processing, obtain approval through IT security and compliance, follow shadow AI governance controls discussed in Chapter 3

Scenario C Key Points:

Issues: Algorithmic bias with documented disparate impact, failure of vendor due diligence (using a vendor with enforcement history), reliance on vendor self-certification without independent audit, no ongoing bias monitoring

Regulations: Australian Privacy Act, precedent from Clearview AI enforcement in Australia (fined by OAIC 2023), potential discrimination law implications, international human rights frameworks

Consequences: Regulatory investigation, privacy commissioner enforcement, discrimination complaints, public backlash, operational disruption if system is suspended

Better approach: Independent bias audit before deployment (not relying on vendor claims), thorough vendor due diligence including enforcement history, disaggregated performance metrics with acceptable thresholds, clear fallback process for false rejections, ongoing monitoring programme

AI Evaluation Teaching Points:

AI chatbots tend to default to GDPR even when the scenario is set in a non-EU jurisdiction — did students catch this?

AI often provides generic responsible AI principles but may miss jurisdiction-specific enforcement precedents (e.g., the specific Clearview AI ruling in Australia)

AI may not fully grasp the practical IAM implications — students with real-world experience should be able to add operational context the AI lacks

The exercise demonstrates both the value of AI as a research accelerator and the necessity of human expertise for jurisdiction-specific, context-aware analysis

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E15: AI Governance Gap Assessment

Type: Structured self-assessment

Duration: 20 minutes

Goal: Assess your own organisation's AI governance maturity against a practical checklist — and leave with concrete action items.

Task

Score your organisation (or a client organisation you know well) on each of the following 10 governance areas. Use the scale:

1 = Not addressed at all | 2 = Aware but no action | 3 = Partially implemented | 4 = Mostly implemented | 5 = Mature and actively maintained

If you're unsure about a specific area, score it as 2 — uncertainty itself indicates a governance gap.

AI Governance Checklist

# | Governance Area | Description | Score (1–5)
1 | AI Inventory | Do you maintain a register of all AI systems in use (including AI features within IAM platforms, AI copilots, and third-party AI services)? |
2 | Risk Classification | Have you assessed AI systems by risk level (per EU AI Act categories or equivalent framework)? Do you know which systems are high-risk? |
3 | Data Governance for AI | Are there controls on what data feeds AI systems? Data minimisation, quality checks, retention policies specific to AI training/inference data? |
4 | Bias Testing & Fairness | Do you test AI systems for bias across demographic groups — both before deployment and on an ongoing basis? |
5 | Transparency & Explainability | Can you explain to users, auditors, and regulators why an AI system made a specific decision? Do affected individuals know AI is involved? |
6 | Vendor AI Assessment | When procuring AI-powered tools, do you assess the vendor's AI practices (training data, bias testing, security, data handling, model updates)? |
7 | Human Oversight | Are there defined processes for human review of AI recommendations? Can humans override AI decisions? Are there appeal mechanisms for affected individuals? |
8 | Shadow AI Controls | Do you have visibility into unapproved AI tool usage? Are there controls to prevent sensitive data from reaching unauthorised AI services? |
9 | NHI / AI Agent Governance | Do you have policies for managing non-human identities, including AI agents — covering authentication, authorisation, lifecycle, and audit? |
10 | Incident Response for AI | Does your incident response plan cover AI-specific failures — biased decisions at scale, data poisoning, prompt injection, model compromise? |

Analysis (5 minutes)

Calculate your total score (out of 50).

Total Score | Maturity Level
40–50 | Leading — Strong foundation. Focus on continuous improvement and emerging areas.
30–39 | Developing — Good progress. Prioritise gaps in high-risk areas.
20–29 | Early Stage — Significant gaps exist. Focus on foundational controls first.
10–19 | Ad Hoc — Governance is largely absent. Urgent action needed on basics.

Identify your 3 lowest-scoring areas. These are your priority gaps.

For each gap, draft one specific action item:

Gap Area | Score | Action Item | Owner | Timeline
(e.g., AI Inventory) | (e.g., 1) | (e.g., Catalogue all AI features in our IAM platform stack by end of Q2) |  |

Mapping to Frameworks

Your governance areas map to established frameworks:

Governance Area | NIST AI RMF | ISO 42001 | EU AI Act
AI Inventory | GOVERN 1.1 | 6.1, A.4 | Art. 6 (risk classification)
Risk Classification | MAP 1.1 | 6.1.2 | Art. 6, Annex III
Data Governance | MANAGE 2.2 | A.7 | Art. 10 (data quality)
Bias Testing | MEASURE 2.6 | A.8 | Art. 9 (risk management)
Transparency | GOVERN 1.5 | A.5 | Art. 13, Art. 50
Vendor Assessment | GOVERN 1.6 | A.9 | Art. 6 (supply chain)
Human Oversight | GOVERN 1.3 | A.6 | Art. 14
Shadow AI | GOVERN 1.1 | 6.1 | Art. 4 (AI literacy)
NHI Governance | MAP 3.5 | A.4 | — (emerging)
Incident Response | MANAGE 4.1 | A.10 | Art. 9 (monitoring)

Takeaway: This assessment is yours to keep. Use it as a starting point for conversations with leadership about AI governance priorities.

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E16: Regulation Mapping with AI Research

Type: Individual + AI tool

Duration: 15 minutes

AI Tool Required: Yes (any free-tier chatbot: ChatGPT, Claude, Gemini, Microsoft Copilot)

Goal: Make regulatory compliance practical by mapping specific AI-powered IAM capabilities to the regulations and frameworks that govern them, using AI as a research assistant and critically evaluating its output.

Task

For each capability, make your own assessment first of the EU AI Act risk classification and other applicable regulations. (5 min)

Paste each capability description into your AI chatbot using the scene-setting prompt below. Ask it to classify the capability and identify applicable regulations. (5 min)

Compare: (5 min)

Is the AI's classification correct? Does it understand the nuance between advisory vs. autonomous AI?

Note where you disagree and why.

Scene-Setting Prompt (paste into your chatbot before each capability)

You are a regulatory compliance specialist with expertise in AI regulation, data
protection, and identity management. I will describe AI-powered IAM capabilities.
For each, analyse: (1) The EU AI Act risk classification (Unacceptable, High-Risk,
Limited Risk, or Minimal Risk) with your reasoning, (2) Other applicable regulations
and frameworks (GDPR, NIST AI RMF, ISO 42001, sector-specific laws), (3) One specific
compliance obligation the organisation must fulfil. Pay attention to the distinction
between "AI recommends, human decides" vs. "AI decides autonomously" — this often
determines the risk classification. Here is the first capability:

The Capabilities

1. Facial Recognition for Remote Customer Onboarding (CIAM)

A financial services company uses AI-powered facial recognition to verify customer identity documents and match selfies during remote account opening.

EU AI Act Classification | Other Applicable Regulations | One Specific Obligation
Your view: |  |
AI's view: |  |
Disagreements: |  |

2. AI Risk Scoring for Employee Login Authentication

An enterprise deploys an AI risk engine that calculates a risk score for every employee login attempt. Scores above a threshold trigger MFA or block access.

EU AI Act Classification | Other Applicable Regulations | One Specific Obligation
Your view: |  |
AI's view: |  |
Disagreements: |  |

3. AI-Powered Behavioural Biometrics for Continuous Authentication

A banking application continuously monitors keystroke dynamics and mouse movement patterns to verify user identity throughout a session.

EU AI Act Classification | Other Applicable Regulations | One Specific Obligation
Your view: |  |
AI's view: |  |
Disagreements: |  |

Safety Reminder: Do NOT include real company names, system details, or sensitive data in your prompts.

Instructor-Led Addition: After individual work, facilitate a 5-minute group comparison. Ask participants which capability generated the most disagreement with the AI and why.

Discussion Guide (For Instructor)

After participants complete the exercise (10 minutes), facilitate a 5-minute comparison and discussion.

Suggested Answers

1. Facial Recognition for Remote Onboarding

EU AI Act: High-Risk (Annex III, Category 1, remote biometric identification). Requires conformity assessment, data quality obligations, transparency, human oversight.

Other: GDPR (biometric data = special category under Art. 9, requires explicit consent or legal basis), eIDAS 2.0 (for EU digital identity wallets), AML/KYC regulations (sector-specific), national data protection laws.

Specific obligation: Conduct a data protection impact assessment (DPIA) under GDPR before deployment, plus conformity assessment under EU AI Act by Aug 2026.

2. AI Risk Scoring for Employee Login

EU AI Act: Likely High-Risk (Annex III, Category 4, AI used in employment and access to work). Risk scoring that determines whether employees can access work systems affects their ability to perform their job.

Other: GDPR Art. 22 (right not to be subject to purely automated decisions with significant effects), labour/employment law, ISO 27001 access control requirements.

Specific obligation: Ensure meaningful human oversight. Employees must be able to challenge and obtain human review of access denials based on AI risk scores (GDPR Art. 22 + EU AI Act Art. 14).

3. Behavioural Biometrics for Continuous Authentication

EU AI Act: Likely High-Risk (biometric identification/categorisation of natural persons). Continuous biometric monitoring raises specific obligations.

Other: GDPR (biometric data = special category, continuous monitoring requires strong legal basis), ePrivacy Directive, labour law (employee monitoring requirements), national biometric laws (e.g., Illinois BIPA-style legislation).

Specific obligation: Obtain explicit, informed consent for continuous biometric monitoring OR establish alternative legal basis. Provide clear privacy notice explaining what data is collected, how it's processed, and retention periods.

Key Discussion Points

All three capabilities are likely High-Risk under the EU AI Act, but for different reasons (biometric identification, employment access, continuous monitoring). This shows how varied the regulatory triggers can be.

The distinction between "AI recommends, human decides" vs. "AI decides autonomously" often determines the risk classification. For capability #2, if the risk engine blocks access automatically (no human in the loop), it strengthens the High-Risk argument.

Multiple regulations apply simultaneously. Compliance is never a single-framework exercise.

AI chatbots may confidently classify capabilities but miss nuances around human oversight levels. Did students catch cases where the AI got the "recommend vs. decide" distinction wrong?

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A

Exercise E17: Capstone — AI-in-IAM Action Plan

Type: Action planning (individual)

Duration: 15 minutes

Goal: Translate course learning into concrete, accountable actions at three time horizons.

Context

Over the past three days, you've covered:

AI fundamentals and how different AI types power IAM capabilities

Prompt engineering for effective, safe AI interaction

AI integrated into IAM platforms, copilots, NHI/agent identity, and shadow AI

Real-world threats, bias, regulatory requirements, and governance frameworks

The evolving role of the IAM professional

Now it's time to commit to action. Not a wishlist — a plan.

Task

Complete the action plan below with specific, concrete commitments at three time horizons. For each, include:

What you will do (be specific)

Why it matters (connect to course content)

How you'll know it's done (measurable outcome)

Tier 1: This Week (One thing to try)

Something you can do in the next 5 working days to apply what you've learned.

Examples:

"Try drafting an access review notification using prompt chaining with a free AI tool (anonymised data only)"

"Identify how many AI features are active in our Entra ID tenant — check Conditional Access risk policies, ID Protection settings"

"Ask our IAM tool vendors about their AI training data practices"

What I Will Do
Why It Matters
How I'll Know It's Done

Tier 2: This Quarter (One initiative to propose)

Something you can champion or propose to your team or management within the next 3 months.

Examples:

"Propose an AI inventory exercise — catalogue all AI features in our IAM platform stack"

"Recommend a shadow AI acceptable use policy for our IAM/security team"

"Suggest a pre-deployment bias testing requirement for any new AI-powered IAM tool"

"Propose building a library of approved, tested prompt templates for common IAM tasks"

What I Will Propose
Why It Matters
How I'll Know It's Done

Tier 3: This Year (One strategic goal)

A longer-term objective that positions your organisation or team for the AI-driven IAM future.

Examples:

"Establish a formal AI governance framework for IAM, aligned with NIST AI RMF or ISO 42001"

"Develop an NHI/AI agent identity policy covering authentication, authorisation, lifecycle, and audit"

"Conduct an EU AI Act readiness assessment for our IAM AI systems before the Aug 2026 deadline"

"Build a business case for an enterprise AI platform to replace ad hoc shadow AI usage"

"Create an AI skills development programme for the IAM team"

What I Will Work Toward
Why It Matters
How I'll Know It's Done

Sharing (5 minutes)

Instructor-Led Addition: Choose one action from any tier and share it with the group: what you're committing to, why you chose it, what challenge you anticipate.

Self-Paced: Write a brief reflection: which tier felt most actionable? Which felt most ambitious? What's your biggest anticipated challenge?

Course Wrap-Up Connection

Your action plans connect directly to the course's key takeaways:

Takeaway | Your Action Demonstrates
AI is integral to modern IAM | Tier 1: Engaging with AI features in your current environment
Responsible AI is non-negotiable | Tier 2: Proposing governance, bias testing, or policy initiatives
AI augments, doesn't replace | Tier 3: Building human-AI collaboration models
Continuous learning is essential | All tiers: Committing to ongoing engagement with AI in IAM

Thank you for your participation. The future of AI in IAM is being built right now — and you're part of it.

Q4: Chapter 4 Knowledge Check

Type: Multiple-choice quiz

Duration: 10 minutes

Goal: Verify understanding of AI threats, responsible AI, regulation, and governance covered in Chapter 4.

Instructions: Select the best answer for each question. Answers are provided at the end for self-scoring. Suggested passing threshold: 7/10.

1. In the Arup deepfake incident, how much money was lost due to an AI-generated video call impersonating company executives?

a) $2.5 million b) $10 million c) $25.6 million d) $100 million

2. What is "prompt injection" in the context of AI security?

a) Injecting malicious code into an AI model's source code b) Crafting inputs that manipulate an AI system into ignoring its safety instructions or performing unintended actions c) Adding extra prompts to improve AI response quality d) A technique for training AI models faster

3. At what accuracy rate do humans detect high-quality deepfakes, according to research?

a) 75% — most people can spot them b) 50% — essentially random chance c) 24.5% — worse than random chance d) 90% — humans are still reliable

4. What is the PRIMARY source of algorithmic bias in AI systems?

a) The programming language used to build the model b) Biased or unrepresentative training data c) The size of the AI model d) The cloud provider hosting the model

5. NIST research found that facial recognition error rates across demographic groups can vary by:

a) 2–5x b) 10–100x c) 1,000x d) No significant variation exists

6. Under the EU AI Act, what is the maximum fine for non-compliance with high-risk AI system requirements?

a) €5 million or 1% of global turnover b) €15 million or 3% of global turnover c) €35 million or 7% of global turnover d) €50 million or 10% of global turnover

7. When does the EU AI Act's high-risk AI system requirements come into effect?

a) Already in effect since 2024 b) February 2025 c) August 2026 d) January 2028

8. Which of the following is a key component of an AI governance framework for IAM teams?

a) Using only open-source AI models b) Maintaining an AI system inventory, conducting bias testing, and ensuring human oversight c) Deploying AI systems without documentation to maintain agility d) Relying entirely on vendor compliance certifications

9. What does "Explainable AI" (XAI) mean in the context of IAM?

a) AI systems that can explain themselves in any human language b) The ability to understand and explain why an AI system made a specific decision (e.g., why access was denied) c) AI systems that generate detailed technical documentation automatically d) A certification standard for AI transparency

10. What is the "non-negotiable" principle for AI in IAM highlighted throughout the course?

a) Always use the most advanced AI model available b) AI should fully replace human decision-making for efficiency c) Human oversight must be maintained — AI augments but does not replace human judgement d) AI systems should operate autonomously without human intervention

Q4 Answers

# | Answer | Explanation
1 | c) | The Arup incident in 2024 resulted in $25.6M lost when an employee was deceived by a deepfake video call impersonating multiple executives.
2 | b) | Prompt injection manipulates AI systems through carefully crafted inputs that override safety guardrails — demonstrated by attacks like EchoLeak (CVSS 9.3).
3 | c) | Research shows humans detect high-quality deepfakes at only 24.5% accuracy — worse than flipping a coin, underscoring why technical controls are essential.
4 | b) | Biased training data is the primary source — if historical data reflects existing inequities, the AI learns and perpetuates those biases.
5 | b) | NIST's landmark study found 10–100x variation in false positive rates across demographic groups in many commercial facial recognition systems.
6 | c) | The EU AI Act imposes fines up to €35 million or 7% of global annual turnover for the most serious violations.
7 | c) | High-risk AI system requirements under the EU AI Act take effect on August 2, 2026.
8 | b) | Effective AI governance includes maintaining an inventory, bias testing, transparency, vendor assessment, human oversight, and incident response planning.
9 | b) | XAI enables organisations to explain AI-driven decisions to users, auditors, and regulators — critical for trust, compliance, and debugging.
10 | c) | Human oversight is the non-negotiable — AI augments human capabilities but humans must retain control over strategy, edge cases, ethics, and accountability.

Score: ___ / 10 | Suggested passing threshold: 7/10

© 2026 Identitrain, Inc. All Rights Reserved.IDT-AI-101 rev A