Leveraging Artificial Intelligence in Identity and Access Management

IDT-AI-101 — Student Guide

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

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

This is the opening slide. Set a welcoming, energising tone. Emphasise that the course is practical and accessible, designed for IAM professionals, not AI engineers. The goal is to make participants feel this is directly relevant to their work in identity and access management, not abstract or overly academic.

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

The primary objective of this course is to equip IAM professionals with a practical understanding of Artificial Intelligence in the context of their work. The course emphasises conceptual clarity, real-world applicability, hands-on practice, and responsible use, enabling participants to navigate the integration of AI into IAM tools and processes effectively.

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

This course is designed for a wide range of professionals within the IAM ecosystem. The content is intentionally geared towards those without a specialised AI background and is framed for an international audience: examples, incidents, and regulatory references are drawn from multiple jurisdictions, not limited to any single country.

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The course is structured to build knowledge progressively: foundations → practical skills → real-world applications → risks and governance → future outlook. 21 exercises (17 activities + 4 quizzes). 13 of 17 activities use AI tools. The "scene-setting prompt" pattern provides pre-written prompts for AI tool exercises to lower the barrier and ensure consistency. In instructor-led delivery, exercises can be enriched with group discussion and pair-sharing.

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This slide reframes the "why AI" question around career opportunity and practical necessity, supported by quantitative data. Key sources: ISC2 2025 Cybersecurity Workforce Study (70% pursuing AI qualifications, AI/ML in top-5 skills), PwC Global AI Jobs Barometer 2025 (56% wage premium). The framing avoids fear-based messaging in favour of empowerment and professional development. Note: ISC2 study covers cybersecurity broadly which includes IAM; the speaker note language is adjusted to be IAM-inclusive.

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This scene-setting slide establishes the 2025–2026 context with hard data. Key statistics: NHI ratio 50:1+ and 97% overprivileged (Entro Security 2025), 82% deploying AI agents / 44% with governance (SailPoint 2025), EU AI Act high-risk deadline Aug 2, 2026. This slide motivates the entire course by showing participants that the topics covered aren't theoretical; they're operational realities they'll face immediately. The framing centres on IAM governance challenges (lifecycle, provisioning, access governance for new identity types) rather than security operations.

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This chapter introduces the fundamental concepts of Artificial Intelligence (AI) most relevant to Identity and Access Management (IAM) professionals. It covers what AI is, the key technical concepts (Neural Networks, LLMs), and the main types of AI learning (Supervised, Unsupervised, Generative). The goal is practical understanding without requiring a technical background. Content is streamlined from the previous version: reinforcement learning and diffusion models receive brief mentions rather than dedicated slides, and real-world incidents anchor the examples.

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Artificial Intelligence, in a practical business context, refers to technology enabling computer systems to perform tasks typically associated with human intelligence. This includes learning from data, making predictions or decisions based on that learning, and processing information like text or images. For IAM, the focus is primarily on leveraging AI for advanced pattern recognition and automating complex tasks at scale, rather than replicating human consciousness.

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Modern IAM faces significant challenges: environments are increasingly complex with hybrid IT and growing application portfolios; non-human identities are multiplying rapidly; governance at scale (access reviews, role management, compliance) is straining manual processes; and there's constant pressure to balance controls against user experience. AI is emerging as a key technology to help address these pressures by enhancing analysis, automating repetitive tasks, and enabling more intelligent, adaptive processes.

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AI offers significant advantages across core IAM functions. Security benefits include advanced anomaly detection and real-time risk assessment. Efficiency gains come from automating access review recommendations, log analysis, policy drafting, and identity verification. User experience improves through adaptive authentication that adjusts friction based on context. Compliance is supported by continuous policy analysis, outlier detection, and automated identification of risky access configurations. This slide consolidates what was previously two separate slides in the earlier version.

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This slide anchors AI capabilities to real-world incidents and shipping products rather than generic descriptions. Key references: Arup deepfake fraud ($25.6M, Hong Kong, 2024), Entrust deepfake attack frequency (every 5 minutes, 2025), SailPoint Harbor Pilot (GA March 2025), PingOne Risk (contextual risk scoring). This approach makes the material concrete and verifiable, building credibility with experienced IAM practitioners who are sceptical of vague AI claims.

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New exercise replacing the original E1 (AI Around Us). This gets AI tools into participants' hands in the first hour, establishing comfort and setting up the prompt engineering chapter. 10 minutes. Uses only general IAM concepts, no sensitive data. Instructor-led sessions can have 2-3 volunteers share which response surprised them.

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Having established what AI is and why it's relevant to IAM, this section introduces the fundamental technological concepts that power modern AI systems. We cover Artificial Neural Networks (ANNs) and Large Language Models (LLMs) at a conceptual level, focusing on what they do and why they matter for specific IAM capabilities. Other model types (diffusion models, reinforcement learning) are mentioned briefly in the recap for completeness.

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Artificial Neural Networks (ANNs or NNs) are a fundamental concept in modern AI, inspired by the way brains process information. They consist of layers of interconnected nodes (neurons). Each layer receives input, performs simple calculations, and passes output to the next layer. This layered structure allows them to learn and represent complex relationships within data.

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ANNs learn by adjusting the "strength" or "weight" of the connections and the "node threshold" or "bias" in the nodes. Initially, these are often random. As the network processes training data, its output is compared to the known correct answer. If the output is wrong, an algorithm adjusts values slightly to improve the result next time. This iterative process of processing data, getting feedback, and adjusting allows the network to gradually "learn" the patterns that lead to correct outputs.

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The primary strength of Neural Networks is their ability to uncover complex, non-linear patterns in data that might be invisible to simpler methods or human analysis. This makes them invaluable for IAM tasks involving pattern recognition at scale: UEBA for behavioural anomaly detection, biometric identity verification, risk scoring for adaptive authentication, and peer group analysis for identity governance. NNs are the engine behind many vendor AI features.

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Large Language Models (LLMs) are a specific type of Artificial Neural Network characterised by their immense size (billions of parameters or "weights") and training on vast corpora of text and code. Their fundamental trained capability is sequence prediction: determining the most probable next word or token given a preceding sequence. While simple sounding, mastering this allows LLMs to understand context, grammar, and factual associations within the language.

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While trained on sequence prediction, the scale of LLMs leads to "emergent" capabilities: complex tasks they can perform without being explicitly programmed for them. These include generating fluent text, question-answering, summarisation, translation, and code writing. These abilities arise from the model learning deep patterns and relationships within the language data. Many well-known AI tools (ChatGPT, Claude, Gemini) are powered by these models.

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LLMs are highly relevant to IAM because many IAM processes involve creating, understanding, or interacting with text-based information. Their primary role is enabling Generative AI applications: drafting documentation, summarising text data (with data privacy caveats), explaining complex topics, powering chatbots, and, increasingly, enabling AI copilot interfaces that let IAM professionals query identity data using natural language. This last capability represents a significant shift in how IAM teams interact with their platforms.

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This consolidated recap covers NNs (pattern recognition for UEBA, biometrics, risk scoring), LLMs (language tasks for drafting, summarisation, copilots), and briefly mentions diffusion models (deepfake context) and reinforcement learning (potential future adaptive auth optimisation). The key message is that different AI approaches serve different purposes, helping participants understand why vendors use different underlying technologies for different features. Streamlined from three separate recap slides in the previous version.

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Exercise E2 (formerly E3). Converted from group activity to individual + AI tool exercise. The scene-setting prompt configures the chatbot as a critical vendor evaluator. Participants compare their own analysis to the AI's, teaching both vendor evaluation skills and AI tool usage. 15 minutes.

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This section covers the three primary types of AI learning most relevant to IAM applications: Supervised (learning from labelled examples), Unsupervised (discovering hidden patterns in unlabelled data), and Generative (creating new content). Reinforcement Learning is omitted as a dedicated topic due to minimal current IAM application; it was briefly mentioned in the recap slide.

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Supervised Learning is defined by its use of labelled datasets during training. The AI learns to map inputs to outputs by identifying patterns associated with each label. Common IAM applications include recognising known threat patterns (phishing, fraud) and predicting outcomes based on labelled historical data (compliance classification). Its effectiveness depends heavily on the quality and quantity of labelled training data.

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Unsupervised Learning works with unlabelled data to autonomously discover patterns, groupings, and anomalies. Its primary IAM application is UEBA: learning baseline behaviour and flagging deviations. It's also used for role mining (clustering users by access patterns) and outlier detection in identity governance. A key advantage is detecting novel threats for which no labelled examples exist. This is emphasised as the most IAM-relevant learning type.

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Generative AI focuses on producing new content rather than just analysing existing data. Primarily powered by LLMs for text-based tasks, it has numerous IAM applications: policy drafting, documentation, summarisation (with data privacy caveats), chatbots, AI copilots, and synthetic data generation. The defining characteristic is its ability to synthesise and create, distinguishing it from analytical AI approaches. Data privacy considerations when using generative AI tools are covered in Chapter 2.

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Selecting the appropriate AI type depends on the problem (classification, anomaly detection, content creation) and the available data (labelled, unlabelled, or prompt-based). Real-world systems frequently combine approaches, e.g., unsupervised anomaly detection feeding supervised classification, or generative AI creating content that humans validate. Understanding these distinctions equips IAM professionals to evaluate vendor claims, understand data requirements, and make informed decisions during procurement.

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Exercise E3 (formerly E4, expanded). Now 6 scenarios instead of 4. The two additions cover the NN vs LLM distinction previously tested in the removed E2 exercise, integrated into the learning types framework. Allow 10 minutes total: 5 for individual work, 5 for review and discussion.

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Chapter 2 covers AI interaction skills for IAM professionals. Structured in two parts: Part 1 builds understanding of modern AI capabilities and foundational concepts (reasoning modes, multimodal, RAG/MCP, classic prompt techniques). Part 2 focuses on actionable skills (thinking modes in practice, structured outputs, system instructions, templates, workflow design). Four exercises provide hands-on practice. Opens with data privacy as the foundational principle before any hands-on work.

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Reframes the chapter from "prompt engineering" to "context engineering," reflecting the 2025-2026 evolution. The key message: the prompt is one component of a larger information environment. Professionals who configure the full context (system instructions, retrieved data, tool definitions, conversation history) get consistently better results. Sets up the chapter structure: Part 1 covers modern AI capabilities and foundational concepts; Part 2 covers practical skills for configuring context effectively.

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Establishes data privacy as the foundational principle before any hands-on AI work. Key data points: 80%+ shadow AI adoption, 34.8% sensitive data input rate, source code as #1 leak type (18.7%). International regulatory coverage (GDPR, CCPA, LGPD, PIPL, PDPA) reflects global audience. Samsung/ChatGPT incident (2023) provides a concrete example. This principle is referenced throughout all subsequent exercises.

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Awareness slide grounding participants in 2026 AI capabilities. Key developments: reasoning/thinking modes (now standard across all major platforms), multimodal input (images, documents, screenshots), expanded context windows (1M+ tokens), structured outputs (JSON mode), and tool use/function calling. Goal is updating participants' mental model, not teaching techniques yet. Sets up the practical skills in Part 2.

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Compressed slide covering four foundational prompting techniques: zero-shot, few-shot, option generation with evaluation, and meta-prompting. Framed as "building blocks that modern systems automate" rather than novel techniques. Each has a one-line IAM example. Understanding these foundations supports effective use of the more sophisticated capabilities covered in Part 2 and helps participants debug AI behaviour when results are unsatisfactory.

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Prompt chaining presented as both practical technique and conceptual precursor to agentic AI. Security alert example: extract facts, identify risks, recommend actions. The "bigger picture" framing connects chaining to AI agent content in Chapter 3, helping participants understand that agents automate what humans do manually with chaining. Exercise E6 provides hands-on practice.

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Two complementary enterprise AI concepts: RAG as the architecture grounding AI in organisational data, and MCP as the emerging integration standard. RAG connected to specific products (Security Copilot, Harbor Pilot). MCP positioned as an IAM governance concern: it defines how AI connects to enterprise systems, making each connection a security and governance surface. MCP introduced by Anthropic (Nov 2024), adopted broadly by mid-2025, donated to Agentic AI Foundation (Dec 2025). Both concepts bridge to Chapter 3 copilot and agent content.

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Merges "Why Prompt Engineering is Crucial in IAM" and both "Common Pitfalls" slides into a single slide. Five pitfalls presented as a quick-reference numbered list with one example each. Compression appropriate for a 2026 audience with some chatbot experience. Core message: prompt quality matters in IAM because the stakes are high. Exercise E4 immediately follows for hands-on practice.

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Participants see the direct impact of prompt quality on output quality by testing their own rewrites live. 10 minutes. In instructor-led sessions, add optional sharing of best rewrites.

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Introduces reasoning/thinking modes as a practical capability for IAM analysis. All major platforms now offer this with configurable effort/thinking budget settings. Specific IAM use cases and clear guidance on when not to use them. This capability is relatively new to most participants and represents one of the biggest practical improvements in AI tools.

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Two practical capabilities: structured outputs (JSON, CSV, schema-conformant responses for downstream tool integration) and multimodal input (images, screenshots, PDFs, diagrams). Each illustrated with specific IAM examples. Structured outputs positioned as the bridge from "AI as writing assistant" to "AI as workflow component." Multimodal input presented as a practical game-changer most participants haven't tried.

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Covers persistent AI workspaces (Claude Projects, Custom GPTs, Gemini Gems). IAM use cases (Policy Reviewer, Compliance Analyst, Access Review Assistant) are concrete enough for participants to create immediately. Platform choice framed around organisational priorities rather than specific model capabilities, as the landscape evolves rapidly. System instructions positioned as "the most powerful lever in context engineering."

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Hands-on lab requiring a free-tier AI tool. Iterative structure (draft, refine, compare) teaches the value of prompt iteration. 20 minutes. Instructors can encourage participants to try thinking modes or structured outputs covered earlier in the chapter. Reinforce data privacy rule throughout.

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Students execute the three-step chain live using a sanitised log snippet from the exercise file. 15 minutes. Reinforces the chaining concept from earlier in the chapter. In instructor-led sessions, optionally add brief group discussion of results.

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Templates presented as practical, reusable prompt structures with four components: purpose, context fields, safety checklist, and example. Connects to earlier system instructions content by showing templates can evolve into AI Project configurations. Safety checklist emphasised as the most important component. Exercise E7 immediately follows.

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Capstone slide tying together all Chapter 2 content. Core message: move from ad-hoc chatbot use to building professional AI workflows. The workflow concept integrates all preceding material: system instructions provide constraints, templates provide structure, safety checklists prevent data leaks, structured outputs ensure consistency, human review maintains accountability. "Living processes" framing emphasises iteration and improvement over time as tools and understanding evolve. Directly precedes Exercise E7 where participants build their first workflow component.

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Participants build a template, test it live, and refine based on output quality. 15 minutes. The bonus step (converting to AI Project system instructions) connects to the system instructions content covered earlier. In instructor-led sessions, have 2-3 students share their templates. This exercise produces a tangible takeaway participants can use immediately in their daily work.

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Chapter 3 is the most significantly restructured section of the course. It now covers four areas reflecting the 2025–2026 reality: (1) AI capabilities integrated into IAM platforms, (2) AI copilots as IAM tools, (3) non-human identity and AI agents as a new identity type, and (4) shadow AI as an IAM governance challenge. All vendor references have been verified against current shipping products.

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This section maps the AI concepts from Chapter 1 (neural networks, unsupervised learning, anomaly detection, generative AI) to concrete features in commercial IAM platforms. The goal is practical recognition: when you see a "risk score" or an "AI recommendation" in a tool, you should understand what's powering it and what its limitations are. All platform examples reference generally available features as of early 2026.

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UEBA is the most mature AI capability in IAM. Machine learning models (typically neural networks using unsupervised learning) establish behavioural baselines per user and entity, then flag significant deviations as anomalies with associated risk scores. The Arup deepfake incident ($25.6M, Hong Kong, 2024) illustrates why AI-driven behavioural detection is essential: traditional controls cannot detect sophisticated AI-powered attacks. Key platforms: Entra ID Protection, PingOne Risk, Okta Behavior Detection, AWS GuardDuty.

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Adaptive authentication leverages real-time risk scores from AI engines to dynamically adjust authentication requirements. High-risk attempts trigger stronger controls (MFA, blocking); low-risk attempts from recognised contexts proceed with minimal friction (passwordless). This optimises both security and user experience. Configuration happens in each platform's policy engine (Conditional Access, Adaptive MFA policies, etc.), with the AI providing the risk signal that drives policy decisions.

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AI-powered Identity Verification (IDV) analyses documents for authenticity, performs facial recognition against selfies, and conducts liveness detection. The threat landscape has escalated dramatically: deepfake attacks on IDV occur every 5 minutes (Entrust 2025), digital document forgeries up 244% YoY and now 57% of all document fraud. Core IAM platforms integrate with specialised IDV vendors (Jumio, Onfido, iProov, Trulioo) via APIs. The verification-vs-forgery arms race is a defining challenge for identity management.

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Intelligent IGA uses machine learning to analyse entitlement data, usage patterns, and peer group comparisons to provide data-driven recommendations during access reviews, suggest role definitions through mining, and detect outlier permissions. This combats "rubber-stamping" in access certification campaigns. Key platforms: SailPoint Harbor Pilot/Predictive Identity (GA March 2025), Saviynt Intelligent Analytics, ForgeRock Autonomous Identity.

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Automated policy analysis uses techniques like automated reasoning to exhaustively analyse access policies (e.g., AWS IAM JSON, Kubernetes RBAC) for misconfigurations, unintended access, and policy violations. AWS IAM Access Analyzer is the primary example: it identifies external/public access, unused permissions, and generates least-privilege policy suggestions from CloudTrail. Key distinction: automated reasoning provides mathematical certainty (not probabilistic results), making it highly reliable for policy validation.

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Ping Identity offers robust AI capabilities through PingOne Risk (UEBA, contextual risk scoring, impossible travel) and PingOne Protect (broader threat detection). These drive adaptive authentication in PingOne/PingFederate. Notably, Ping's "Identity for AI" (announced Nov 2025, GA early 2026) addresses AI agent identity management with a centralised control plane, MCP Gateway, and secretless authentication, positioning Ping at the forefront of the emerging NHI/agent identity challenge. (Slightly more prominent treatment per partner relationship.)

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Microsoft Entra provides comprehensive AI capabilities: Entra ID Protection (ML-driven risk detection), Conditional Access (policy engine consuming risk signals), and Verified ID (verifiable credentials with IDV partner integration). Security Copilot (GA 2025) adds natural language IAM querying. Entra Agent ID (preview May 2025, expanded Nov 2025) provides first-class AI agent identity management (Agent Registry, Conditional Access for agents, Identity Governance for agents) as part of Agent 365. Microsoft treats agents as digital teammates requiring the same identity controls as humans.

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Okta: Adaptive MFA + Behavior Detection for risk-based auth, Identity Threat Protection for real-time detection. Auth0 for AI Agents (GA Oct 2025) provides developer tools for agent identity security. Okta for AI Agents + Cross App Access (XAA) extends OAuth for agent-to-app interactions.

AWS: IAM Access Analyzer (automated reasoning, policy generation), GuardDuty (ML anomaly detection), Cognito (adaptive auth). Bedrock AgentCore (GA Oct 2025) is an enterprise agentic platform with AgentCore Identity for OAuth-based agent authentication. Both vendors reflect the industry trend of AI agent identity as a core capability.

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SailPoint: Harbor Pilot (GA March 2025) introduced AI agents for identity security with 50% customer adoption in 30 days. Predictive Identity provides ongoing AI recommendations for access reviews and role mining. Agentic Identity Security extends IGA to AI agent governance. The broader industry trend is clear: AI is now standard across all major IAM vendors (CrowdStrike, Saviynt, ForgeRock, Delinea, BeyondTrust, CyberArk, etc.). The differentiator is no longer whether a vendor has AI, but how well and transparently it operates.

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v4 new slide addressing review feedback about missing cost/licensing discussion. Covers licensing models (per-user, consumption/SCU, per-transaction, freemium/enterprise), integration costs (deployment, APIs, SSO/SCIM), data readiness costs (permission cleanup, data quality), ongoing costs (model updates, bias auditing, governance, upskilling), and hidden costs (vendor lock-in, shadow AI cost of inaction at $670K premium). Key message: AI adoption is a full-lifecycle investment. This slide is particularly relevant for the IAM Program & Product Manager segment of the target audience.

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New exercise replacing the original E9 (Feature Spotting). This exercise gets participants using AI tools to simulate the copilot experience, directly reinforcing the RAG concept. It bridges naturally into the copilot section (3.2) and the overpermissioning discussion. 15 minutes. Individual + AI tool.

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Exercise E9 (formerly E10). Participants first make their own decisions, then compare against the AI's analysis using a scene-setting prompt. Fully solo. The Eva scenario bridges to Section 3.3 on NHI/Agent Identity. 15 minutes.

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AI copilots are natural language interfaces built on RAG + LLM + platform APIs, enabling IAM teams to query identity data and execute workflows using plain language. Microsoft Security Copilot (GA 2025) covers the Entra/Defender/Sentinel ecosystem. SailPoint Harbor Pilot (GA March 2025) provides AI agents for identity security workflows. This represents a fundamental shift from dashboard-based to conversational interaction with identity data. Bridges from the RAG concept introduced in Chapter 2 slide 2.13.

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Practical AI copilot capabilities for IAM teams: querying identity data via natural language, investigating incidents through conversation, generating workflows (Harbor Pilot can execute within governance guardrails), explaining policy decisions by tracing evaluations, and drafting documentation. Critical security principle: copilots operate within the user's existing permissions and do not bypass access controls.

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AI copilots amplify the pre-existing overpermissioning crisis: 95% of permissions go unused, and copilots make overshared content instantly discoverable via natural language. The US House banned Microsoft Copilot (March 2024) over data leakage risk; similar concerns in UK/EU. Key message: permission cleanup is a prerequisite for safe copilot deployment. IAM functions (least privilege, access reviews, entitlement cleanup) become critical enablers of AI copilot adoption, not afterthoughts.

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General-purpose LLMs remain useful for IAM tasks outside integrated platform capabilities, but the Chapter 2 golden rule applies absolutely: never input sensitive data into public AI tools. Safe use cases with proper anonymisation: log summarisation, policy drafting, compliance documentation, training materials, regulatory explanation, high-level architecture brainstorming. Human review is mandatory for all AI-generated content. This section is intentionally compressed (from 6 slides in v2 to 2) since the data privacy principle was established in Chapter 2.

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Safe workflow for general AI tools: isolate data internally → extract relevant snippets → anonymise all identifiers → prompt with anonymised content → verify output against actual environment. Key limitations: AI lacks specific environmental context, outputs based on general patterns may be incorrect, and this approach supplements but never replaces expert human analysis. If anonymisation isn't possible, use an approved enterprise AI platform with contractual privacy guarantees instead of public tools.

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Non-human identities outnumber human identities 50:1 to 144:1 in enterprises. 97% are overprivileged, only 29% rotate credentials within recommended timeframes. Real breaches driven by NHI compromise: Microsoft Midnight Blizzard (legacy OAuth app, no MFA → leadership email access), Snowflake campaign (stolen service credentials → 165+ orgs), Internet Archive (exposed GitLab token → full source and user data). NHI governance is the #1 emerging IAM priority.

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AI agents differ fundamentally from traditional NHIs: autonomous decision-making, dynamic access scope, delegation chains, cross-system access. Adoption is widespread (82-91% of organisations) but governance lags dramatically (44% have frameworks, only 10% have NHI strategy). The adoption-governance gap is the core IAM challenge. This positions AI agent identity management as a critical emerging capability for IAM professionals.

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Emerging AI agent identity lifecycle: Registration (identity + owner + purpose) → Authentication (OAuth 2.1, short-lived tokens, secretless) → Authorisation (fine-grained, least privilege, human approval thresholds) → Monitoring (continuous logging, anomaly detection) → Governance (reviews, rotation, decommissioning). Standards: MCP, OAuth 2.1, OpenID Federation. Vendor solutions: Ping Identity for AI (GA early 2026), Entra Agent ID (preview), Auth0 for AI Agents (GA Oct 2025), Bedrock AgentCore (GA Oct 2025), SailPoint Agentic Identity Security.

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Practical IAM implications of AI agents: lifecycle management at machine speed, credential rotation in hours (secretless where possible), governance frameworks defining autonomous vs. human-approved actions, complete accountability and audit trails, cross-platform governance. The fundamental unresolved question: accountability when an agent causes harm. Practical advice: start with discovery: agents are likely already operating in your environment even without formal deployment.

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v4 revision: Converted from group-only to individual + AI tool exercise. Scene-setting prompt configures the chatbot as an IAM governance consultant analysing the org profile. Participants critically evaluate the AI's analysis and combine it with their own expertise. 20 minutes. In instructor-led sessions, add optional group presentation and peer-critique.

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v4 revision: Converted from group design to individual + AI-assisted drafting. Participants use the chatbot to generate a first draft, then critically review and improve it. The common gaps checklist from the instructor reference helps self-paced learners self-critique. 25 minutes. In instructor-led sessions, add optional peer-critique.

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Shadow AI (employees using unapproved AI tools at work) is already widespread: 80%+ adoption, 34.8% entering sensitive data, source code with secrets as #1 leak type (18.7%). Real incidents: Samsung engineers/ChatGPT source code leak (South Korea, 2023), CISA employees using unauthorised tools (US, 2024). This is a global, cross-industry problem and fundamentally an IAM governance challenge involving access controls, data classification, and acceptable use policies.

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IAM's role in shadow AI governance: access controls for AI tools, SSO integration for approved platforms, updated acceptable use policies, monitoring/discovery through CASB and network telemetry, DLP integration for sensitive data detection. Shadow AI incidents carry $670K average cost premium. Pragmatic approach: don't just ban — provide approved alternatives. If employees don't have safe AI tools, they'll use unsafe ones. IAM enables the governance framework that makes safe AI adoption possible.

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v4 revision: Converted from team competition to individual + AI expansion. Solo brainstorm first (5 min), then AI-assisted expansion (10 min). Target: 15+ methods. In instructor-led sessions, optional competitive scoring. The exercise still surfaces the breadth of shadow AI vectors and positions IAM controls as the primary defence.

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Chapter 4 addresses the critical non-technical dimensions of AI in IAM: threats, ethics, regulation, governance, and future trends. It opens with concrete, evidence-based threats (§4.1) to establish urgency, then examines bias and fairness (§4.2), followed by the regulatory and governance landscape (§4.3), and closes with future directions and the evolving professional role (§4.4). Every section is anchored in real incidents, documented enforcement actions, and active regulatory frameworks, reflecting the 2025–2026 reality.

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AI is inherently dual-use: the same capabilities that enhance IAM defences can be weaponised by adversaries. Pattern recognition aids both anomaly detection and evasion techniques. Language generation powers both policy drafting and sophisticated phishing. Image synthesis enables both synthetic training data and deepfake identities. Effective security requires understanding both sides of this equation. This framing sets up the specific threat categories that follow.

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AI-powered social engineering represents a step-change in threat sophistication. Key documented incidents include the $25.6M Arup deepfake video call fraud (Hong Kong, 2024), the Ferrari CEO voice clone attempt (Italy, 2024), the FAMOUS CHOLLIMA North Korean IT worker infiltration campaign using AI personas across 320+ companies (global, 2024–2025, per CrowdStrike), and GoldPickaxe biometric-stealing malware (Thailand/Vietnam, 2024). Aggregate deepfake fraud exceeded $200M in Q1 2025. These incidents directly challenge traditional IAM controls: callback verification, video identity checks, and biometric authentication all face new threat vectors.

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Prompt injection is an emerging attack class with direct IAM implications. EchoLeak (CVE-2025-32711, CVSS 9.3, June 2025) demonstrated the first zero-click data exfiltration from Microsoft 365 Copilot via hidden email instructions processed by RAG. LOLCopilot (Black Hat USA 2024, Michael Bargury/Zenity) showed weaponisation of Copilot for data theft, payment fraud, and phishing. ASCII Smuggling uses invisible Unicode to hide injection payloads. The critical IAM connection: AI copilots inherit user permissions, so prompt injection effectively grants attackers the compromised user's access scope. This reinforces the need for least-privilege principles discussed in Chapter 3's overpermissioning slide.

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Synthetic identity fraud combines real identity fragments with AI-fabricated data to create non-existent persons. ProKYC ($629/year, discovered by Cato Networks Oct 2024) generates synthetic passports and deepfake videos that bypass KYC liveness checks. Fake documents comprised 50% of identity fraud attempts in 2024. New account fraud losses exceeded $5.3B in 2023 (AARP). This is the fastest-growing financial crime category. IAM impact: challenges document-based IDV, CIAM registration, and any verification process assuming document authenticity. Defences require multi-layered approaches: document forensics, third-party database cross-referencing, behavioural analysis, and advanced liveness detection.

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Attacks against AI systems target the models and data underlying IAM security controls. Data poisoning corrupts training data to manipulate model behaviour, particularly concerning for continuously learning UEBA systems. Adversarial examples craft inputs that bypass AI perception while appearing normal to humans, threatening biometric authentication and anomaly detection. Model extraction/inference attacks probe AI systems to steal IP or reveal sensitive training data. The IAM implication: AI-based security controls can be subverted, creating false confidence. Defending requires model validation, monitoring for performance drift, input sanitisation, and fallback mechanisms.

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Effective defence against AI-related threats requires acknowledging AI's limitations. Core principles: defence-in-depth (never rely solely on AI controls), continuous monitoring of AI system performance (detecting poisoning or degradation), incident response planning that accounts for AI failures, and updated human awareness training addressing AI-enhanced threats like deepfakes and prompt injection. The shift from "AI handles security" to "AI is one layer in a resilient security architecture" is the key mindset change for IAM teams.

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

Algorithmic bias produces systematically unfair outcomes through three primary mechanisms: biased training data (reflecting historical inequalities or demographic underrepresentation), flawed algorithm design (using features or proxies that correlate with protected characteristics), and feedback loops (where biased outputs reinforce and amplify the original bias through skewed data collection). Bias is typically unintentional but produces measurable, real-world harm. Understanding these mechanisms is the first step toward effective mitigation, particularly important in IAM where AI-driven decisions affect individuals' access to services and resources.

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Bias in AI systems used for IAM functions is extensively documented. NIST studies show 10–100x false positive rate disparities in facial recognition across demographics. Multiple wrongful arrests in the US and UK have been linked to facial recognition errors. Enforcement actions against biometric bias span multiple jurisdictions: FTC vs Rite Aid (US), Clearview AI fines in the UK, France, Italy, Australia, and Greece. IAM-specific bias risks include IDV false rejection disparities, risk scoring proxies, behavioural biometric variation, and voice recognition performance gaps. The global enforcement trend signals that organisations deploying biased AI in IAM face real legal and financial consequences.

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Bias mitigation requires a multi-layered, ongoing approach: (1) ensuring training data diversity and representation, (2) conducting regular bias testing with disaggregated performance metrics across demographic groups both pre- and post-deployment, (3) implementing quantitative fairness metrics with monitoring thresholds, (4) promoting explainable AI to make bias visible in decision factors, and (5) establishing genuine human oversight and accessible appeal mechanisms. The emphasis on "ongoing" is important: bias is not a one-time problem with a one-time fix. Models drift, user populations change, and new biases can emerge over time.

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Explainable AI (XAI) addresses the "black box" problem by making AI reasoning visible and understandable. In IAM, XAI is essential for: building user and administrator trust, enabling effective debugging when AI errs, facilitating bias detection by revealing decision factors, supporting accountability to auditors and regulators, and meeting regulatory transparency requirements (particularly EU AI Act Article 13 for high-risk systems). Practical XAI implementations include risk score factor breakdowns, access recommendation explanations with supporting data, and policy evaluation traces. XAI transforms AI-driven IAM decisions from opaque outputs into transparent, auditable reasoning.

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

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

This slide consolidates data privacy risks discussed across earlier chapters into a unified view, reinforcing key concerns before the regulatory discussion. Risks include unauthorised access to AI systems and data pipelines, misuse of collected data beyond governance scope, inference attacks revealing training data, public AI tool exposure, over-collection violating minimisation principles, and insufficient transparency about AI data processing. The slide connects to Chapter 2's golden rule, Chapter 3's overpermissioning discussion, and §4.1's coverage of attacks against AI systems.

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Best practices for AI data handling in IAM build on established privacy and security principles: data minimisation (collect only what the AI needs), anonymisation/pseudonymisation (particularly for training data), securing AI-specific infrastructure (pipelines, models, APIs), thorough vendor due diligence (data handling, residency, model training usage), access controls for AI systems themselves, transparent and specific user communication, and defined retention/deletion policies for AI data. These practices form the operational foundation for meeting regulatory requirements discussed in the following slides.

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The EU AI Act is active law with a phased implementation timeline. Prohibited practices are enforceable since Feb 2025. GPAI obligations since Aug 2025. High-risk system obligations take effect Aug 2, 2026, the critical deadline for IAM. IAM-relevant high-risk categories include remote biometric identification, critical infrastructure AI, employment-related AI, and essential services access. Obligations include data quality, transparency, human oversight, robustness, and conformity assessments. Fines up to €35M / 7% global turnover. Extraterritorial application. Finland is the first EU member state with operational enforcement. IAM teams must assess their AI systems against Annex III categories and prepare compliance programmes now.

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The regulatory landscape for AI extends well beyond the EU AI Act. Existing data protection laws (GDPR, UK GDPR, LGPD, PIPL, DPDPA, PDPA) apply to personal data used in AI systems. AI-specific frameworks include the NIST AI RMF (voluntary, US), ISO/IEC 42001 (certifiable international standard), and the Colorado AI Act (state law, effective June 30, 2026 after delay). Singapore's Model AI Governance Framework provides practical guidance. Organisations operating internationally face overlapping, sometimes conflicting requirements, making multi-jurisdictional compliance planning essential.

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A practical AI governance framework for IAM teams includes eight core components: AI inventory and risk classification, risk assessment (bias, privacy, security, operational), AI-specific data governance, vendor AI assessment criteria, deliberate human oversight design, ongoing bias auditing programmes, AI-specific incident response playbooks, and continuous monitoring with drift detection. This framework maps to NIST AI RMF (Govern, Map, Measure, Manage) and ISO 42001 controls. The emphasis is on practical, ongoing governance rather than one-time compliance exercises, reflecting the dynamic nature of AI systems and the evolving regulatory landscape.

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

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

Future AI in identity lifecycle management moves from assistance to orchestration: AI-predicted joiner access based on multi-factor analysis (not just role templates), proactive mover access adjustment recommendations triggered by organisational signals, correlated leaver deprovisioning beyond simple termination dates, continuous AI-driven role engineering maintaining least privilege over time, and self-tuning systems detecting and remediating configuration drift. All require robust governance frameworks; the automation capabilities must be paired with appropriate human oversight and approval mechanisms discussed in earlier sections.

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AI-enhanced Zero Trust evolves from point-in-time verification to continuous, dynamic authentication throughout user sessions. Real-time monitoring of behaviour, device posture, and context enables adaptive responses to mid-session risk changes, including step-up authentication, access restriction, or session termination. Hyper-personalised access could dynamically adjust data visibility based on real-time risk rather than binary allow/deny decisions. Dependencies include mature UEBA, endpoint integration, AI risk modelling, and robust privacy governance for continuous behavioural monitoring.

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AI transforms the IAM professional role rather than replacing it. Emerging skill requirements include AI literacy, prompt engineering, AI governance, critical vendor evaluation, NHI management, and data fluency. Career data supports investment in these skills: AI/ML is now a top-5 in-demand cybersecurity skill (ISC2 2025), with a 56% wage premium and 70% of professionals pursuing AI qualifications. The future model is human-AI collaboration: AI handles scale and pattern matching while humans provide strategy, ethics, governance, and accountability.

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Human oversight is the foundational principle for responsible AI deployment in IAM. AI augments human capabilities but cannot replace strategic thinking, ethical judgement, edge-case handling, or accountability. Every AI system should have defined answers to: when does a human review? Who can override? How do affected individuals appeal? This principle is reinforced by regulatory requirements (EU AI Act Art. 14 mandates human oversight for high-risk systems), practical resilience (AI systems can fail, be fooled, or drift), and stakeholder trust. Framing: human oversight is pro-resilience, not anti-AI.

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

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

Course summary recapping four chapters: Ch1 AI Foundations (concepts, engines, learning types), Ch2 Working with AI (data privacy golden rule, context engineering, 2026 AI capabilities, foundational techniques, thinking modes, structured outputs, system instructions, AI workflow design, hands-on labs), Ch3 AI in IAM Today (integrated platforms, AI copilots, NHI/agent identity, shadow AI), Ch4 Responsible AI & Road Ahead (evidence-based threats, bias/fairness with international enforcement, EU AI Act and global regulation, governance frameworks, future trends, evolving professional role). The course covered 82 slides and 17 exercises plus 4 quizzes across approximately 3 days.

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Eight key takeaways consolidating the course's core messages: AI's current integration into IAM, the importance of conceptual understanding, prompt engineering as practical skill with data privacy as foundation, evidence-based threats requiring real defences, responsible AI as non-negotiable (bias, privacy, regulation, explainability), human oversight and judgement remaining essential, the IAM profession evolving with new skill demands and career opportunities, and continuous learning as essential given the pace of change.

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Next steps and resources to support continued learning: execute the personal action plans from Exercise E17, experiment safely with AI tools, and stay current with the rapidly evolving landscape. Key resources include governance frameworks (NIST AI RMF, ISO 42001, EU AI Act), vendor-specific AI documentation, industry conferences (RSA, Identiverse, Gartner IAM Summit), professional communities (ISC2, ISACA), and research resources (NIST FRVT, MITRE ATLAS, OWASP Top 10 for LLMs). The slide reinforces responsible application: data privacy first, critical validation of AI outputs, and advocacy for governance and oversight within participants' organisations.

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Closing slide. The course has equipped participants with foundational AI knowledge, practical prompting skills, awareness of integrated and emerging AI capabilities in IAM, understanding of threats and ethical considerations, familiarity with the regulatory landscape, and a concrete action plan for applying their learning. The emphasis on continuous learning and responsible application carries through to the final message.

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