Cyber Security Training

Secure GenAI: Threat Modelling, Governance & Responsible AI

Enterprise-grade security, governance, and Responsible AI controls to protect, govern, and operate GenAI and agentic systems safely at scale.
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  • Level : Intermediate to Advanced
  • Duration : 32 hours
  • Hands-on Ratio : 55–65%
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Building Strategic Influence in Matrix Organizations

GenAI introduces new threat vectors and governance challenges. Secure GenAI prepares organizations to identify, assess, and mitigate security, privacy, and ethical risks associated with GenAI systems.

The course covers threat modeling for GenAI, data exposure risks, prompt injection, misuse scenarios, and responsible AI principles. Participants learn how to embed controls across design, deployment, and usage.

The outcome is a practical security and governance mindset—enabling GenAI innovation while protecting enterprise assets, users, and trust.

Recommended participant setup

Azure subscription, Microsoft Foundry and Azure OpenAI access, Azure AI Content Safety, Azure Monitor and Log Analytics, sample application with RAG corpus

AI-First Learning Approach

This course follows Cognixia’s AI-first, hands-on learning model - combining short concept sessions with practical labs, real workplace scenarios, and embedded governance to ensure safe, scalable, and effective skill adoption across the enterprise.

Business Outcomes

Organizations enrolling teams in this course can achieve

  • Stronger Security Posture: Reduced exposure to GenAI-specific threats through structured threat modelling and defense-in-depth controls
  • Governance and Compliance Readiness: Auditable governance frameworks, policies, and evidence aligned to regulated enterprise environments
  • Responsible AI at Scale: Measurable safety, privacy, and accountability practices embedded into GenAI delivery and operations

Why You Shouldn’t Miss this course

By the end of this course, participants will be able to:
  • Understand / Explain GenAI security risks, threat models, and Responsible AI principles in enterprise contexts
  • Apply structured threat modelling techniques to LLM, RAG, and agentic systems
  • Analyze / Evaluate security, safety, and compliance risks across the GenAI lifecycle
  • Create governance artifacts including threat models, risk registers, policy packs, and audit evidence
  • Implement secure-by-design, governance-ready practices for enterprise GenAI systems

Recommended Experience

Participants should have a solid understanding of security fundamentals such as identity and access management, networking, and threat modelling. Basic familiarity with GenAI concepts and LLM-based applications is expected to effectively apply security and governance practices.

Structured for Strategic Application

Bloom-aligned objectives
  • Understand: why GenAI systems introduce new security and governance risks
  • Analyze: where traditional AppSec controls are insufficient
  • Design: a security-first reference architecture for GenAI applications
Topics
  • GenAI system anatomy and attack surfaces: prompts, context, retrieval, tools, memory, endpoints, telemetry
  • Threat categories:
    • prompt injection and instruction hijacking
    • data leakage (PII, secrets, proprietary information)
    • retrieval poisoning and misinformation injection
    • tool misuse and unauthorized actions
    • insecure model access and endpoint abuse
    • operational risks: drift, regressions, policy misconfiguration
  • Security objectives for GenAI:
    • confidentiality, integrity, availability
    • safety, compliance, auditability, non-repudiation
Labs
  • Lab 1.1: GenAI risk inventory — Build a risk inventory for an enterprise assistant (data sources, users, tools, actions).
  • Lab 1.2: Security architecture blueprint — Draw a reference architecture with trust boundaries and control points.
Bloom-aligned objectives
  • Apply: structured threat modelling methods to GenAI systems
  • Analyze: threats across components and trust boundaries
  • Create: a prioritized mitigation plan with owners and timelines
Topics
  • Threat modelling approach:
    • system decomposition, trust boundaries, data flows
    • threat identification across prompts/retrieval/tools/memory
    • impact/likelihood scoring and prioritization
  • GenAI threat deep dives:
    • direct prompt injection and jailbreak patterns
    • indirect prompt injection through retrieved documents
    • data exfiltration via tool calls or retrieval overreach, cross-tenant data access failures
    • model endpoint abuse (rate, brute force, extraction attempts), prompt and tool schema leakage
  • Control selection and placement:
    • pre-processing, retrieval-time controls, generation-time controls, post-processing validation
Labs
  • Lab 2.1: Full threat model workshop — Create a complete threat model with data flow diagrams and trust boundaries.
  • Lab 2.2: Mitigation backlog — Convert threats into a prioritized backlog with severity, owners, and implementation roadmap.
Bloom-aligned objectives
  • Understand: why retrieval introduces unique integrity and confidentiality risks
  • Implement: data boundary controls and grounded response policies
  • Evaluate: RAG-specific security regressions
Topics
  • RAG risks:
    • retrieval overreach (exposing unauthorized documents)
    • retrieval poisoning and misinformation injection
    • malicious instructions embedded in content (indirect injection), citation manipulation and fabricated sources
  • Secure retrieval patterns:
    • filter-first retrieval (tenant/user/role labels), metadata-based access control enforcement
    • document classification and sensitivity labels, retrieval provenance and audit trails
  • Grounding and refusal policies:
    • evidence thresholds and “insufficient evidence” safe-fail behavior
    • citation validation and snippet verification
  • Data handling:
    • PII/PHI redaction before indexing
    • encryption and key management expectations, secure content ingestion pipelines with integrity checks
Labs
  • Lab 3.1: Secure retrieval policy — Implement and test access filters and validate zero cross-tenant leakage.
  • Lab 3.2: Indirect injection simulation — Inject malicious instructions into documents and validate defenses (instruction hierarchy + validators).
  • Lab 3.3: Citation integrity gate — Implement post-generation checks to block answers without valid citations.
Bloom-aligned objectives
  • Design: safe tool access patterns with least privilege
  • Implement: guardrails for tool invocation and side-effect control
  • Analyze: tool misuse failures and escalation paths
Topics
  • Tool risk landscape:
    • privilege escalation through tools
    • unauthorized actions (refunds, account changes, approvals)
    • prompt-induced tool misuse, tool output poisoning into future decisions
  • Tool governance controls:
    • allowlisted tools and scoped permissions
    • typed schemas and strict argument validation
    • timeouts, retries, circuit breakers, idempotency keys and side-effect protections
  • Human-in-the-loop patterns:
    • approvals for high-risk actions
    • two-person rule and segregation of duties, step-up authentication patterns (conceptual)
  • Auditability:
    • full tool call logs (arguments, outputs, actor identity, timestamps)
    • decision trace capture and reproducibility
Labs
  • Lab 4.1: Tool allowlist gateway — Build a tool gateway policy with validation, permission checks, and auditing.
  • Lab 4.2: High-risk action approval flow — Implement an approval workflow for destructive actions and validate enforcement.
  • Lab 4.3: Side-effect safety drill — Simulate tool timeouts and retries; validate idempotent behavior and no duplicate actions.
Bloom-aligned objectives
  • Create: a GenAI governance framework aligned to enterprise controls
  • Apply: change control to prompts, policies, tools, and models
  • Evaluate: audit readiness and evidence completeness
Topics
  • Governance structure:
    • roles and responsibilities (product, security, legal, compliance, engineering)
    • approval checkpoints and decision rights
  • Policy pack design:
    • acceptable use policy for GenAI
    • data usage and retention policy, tool access policy and escalation rules
    • model selection and validation policy, logging and audit policy
  • Risk management:
    • risk register formats, periodic reviews, control effectiveness scoring
    • third-party/vendor risk (model providers, SaaS tool integrations)
  • Change control:
    • versioning prompts/flows/agents/tool schemas
    • staged environments and promotion gates
    • rollback and emergency change procedures
Labs
  • Lab 5.1: Governance playbook — Create a governance playbook (RACI, approvals, risk reviews, and audit artifacts).
  • Lab 5.2: Control mapping — Map GenAI controls to internal security/compliance standards and define evidence requirements.
  • Lab 5.3: Change control workflow — Design a change-control pipeline for prompt/tool/model changes with gating and audit logs.
Bloom-aligned objectives
  • Understand: Responsible AI principles and their practical implications
  • Apply: safety and privacy-by-design measures for LLM applications
  • Create: measurable acceptance criteria for Responsible AI
Topics
  • Responsible AI principles in practice:
    • safety and harm prevention
    • privacy and data minimization, fairness and bias considerations
    • transparency: disclosures, citations, limitations
    • accountability: ownership, review, escalation
  • Safety controls:
    • content moderation concepts (inputs and outputs)
    • policy-based refusal behavior and safe completion templates
  • Privacy engineering:
    • data minimization for prompts and logs
    • secure redaction of sensitive data in telemetry, retention and deletion workflows
  • User experience safeguards:
    • disclaimers and user guidance
    • safe suggestions and escalation to human experts
Labs
  • Lab 6.1: Responsible AI acceptance criteria — Define measurable criteria for safety, privacy, and transparency for a target use case.
  • Lab 6.2: Transparency design — Implement a response format with citations, limitations, and escalation guidance.
  • Lab 6.3: Privacy logging policy — Define what is logged, what is redacted, retention windows, and review workflow.
Bloom-aligned objectives
  • Evaluate: system robustness using adversarial and regression suites
  • Analyze: failure clusters and create prevention mechanisms
  • Create: a continuous validation plan integrated with release cycles
Topics
  • Testing strategy:
    • unit tests for tool schemas and validators
    • regression tests for prompts and workflows
    • adversarial testing for injection and data leakage
  • Red-teaming:
    • test packs: jailbreak attempts, indirect injection, tool misuse, cross-tenant access attempts
    • scoring and reporting
  • Release gating:
    • quality gates and safety gates
    • blocking criteria and rollback triggers
Labs
  • Lab 7.1: Red-team test pack — Build and run an adversarial test set and define pass/fail thresholds.
  • Lab 7.2: Safety regressionAG pipeline — Implement a CI gate that blocks releases on safety regressions (policy violation, leakage, unsafe tool calls).
Bloom-aligned objectives
  • Apply: operational monitoring and alerting patterns
  • Analyze: incidents and perform structured postmortems
  • Create: incident playbooks for GenAI-specific failures
Topics
  • What to monitor:
    • prompt injection signals, refusal rates, anomalous tool activity
    • access violations, unusual retrieval patterns, token spikes
    • safety incidents and policy violations
  • Incident response:
    • containment (disable tools, restrict retrieval scope, roll back prompt versions)
    • investigation (trace review, tool logs, retrieval logs)
    • communication and documentation for audit
  • Postmortems:
    • root cause analysis, corrective actions, regression tests, governance improvements
Labs
  • Lab 8.1: Incident drill — Run a simulated injection + data leakage incident; execute containment and rollback steps.
  • Lab 8.2: Postmortem template — Produce a complete postmortem report with corrective action backlog and prevention gates.
Deliverable
A complete security and governance package for a GenAI application that includes:
  • Threat model + prioritized mitigations
  • Governance playbook + control mapping + audit evidence checklist
  • Responsible AI acceptance criteria + validation plan
  • Red-team test pack + CI safety gate design
  • Incident response runbooks and rollback triggers
Tools and platforms used
  • Threat modelling templates and risk registers (enterprise-ready formats)
  • Security control catalog (IAM, network security, data security, logging/audit, application security)
  • Adversarial testing and red-teaming harness (prompt packs + tool misuse simulations)
  • Evaluation datasets and gating strategies (offline + continuous validation)
  • Governance artifacts (RACI, approvals, change control, audit evidence pack)
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Why Cognixia for This Course

  • Deep focus on securing and governing real-world GenAI and agentic systems in regulated enterprises
  • Hands-on, artifact-driven delivery that produces enterprise-ready security and governance outputs
  • Responsible AI and compliance embedded across design, deployment, and operations
  • Proven experience delivering secure, scalable AI transformation programs for global organizations

Mapped Official Learning

Secure GenAI: Threat Modelling, Governance & Responsible AI - Course @cognixiaSecure GenAI: Threat Modelling, Governance & Responsible AICertified Information Systems Security ProfessionalCertified Information Systems Security Professional (CISSP)Iot-Security-and-Penetration-TestingIoT Security and Penetration TestingInternet-of-Things-Security-Expert-Internet of Things Security Expert TrainingIoT-SecurityIoT Security Training
Explore Trainings

Designed for Immediate Organizational Impact

Includes real-world simulations, stakeholder tools, and influence models tailored for complex organizations.

Instructor-Led Enterprise Training Expert-led sessions guide teams through real GenAI security, governance, and Responsible AI challenges.
Enterprise-Ready Use Cases Hands-on scenarios reflect regulated enterprise environments, including threat modelling, audits, and incident response.
High Hands-On Learning Ratio Participants produce tangible deliverables such as threat models, policy packs, red-team tests, and runbooks.
Responsible & Scalable AI Adoption Built-in focus on security, governance, and Responsible AI enables safe enterprise-wide GenAI deployment.

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Frequently Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

Yes. The course includes technical security controls, threat modelling, and validation practices for GenAI systems.
Participants should have foundational security knowledge and basic understanding of GenAI applications.
Yes. The course is designed for consistent, auditable adoption across large enterprise environments.
Approximately 55–65% of the course consists of hands-on labs, red-team simulations, and artifact creation.
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