Toll free1800 1020 418Fees & syllabus ↗SPECIALIST PROGRAM · LIVE ONLINE · ENGLISH
Assess cyber risks from GenAI and autonomous agents, verify controls and build an auditable governance portfolio using NIST, OWASP and evidence from realistic security scenarios.
Cyber-risk and GRC professionals, security managers, auditors, compliance teams and technical staff moving into AI security governance.
Basic understanding of business risk, security controls and evidence review. No coding prerequisite; technical control demonstrations are explained through business decisions and risk scenarios.
Allow 2–4 hours each week for evidence review and portfolio assignments. Live time totals 24–36 hours across 24 sessions; assignments and preparation do not count toward those hours.
Use a laptop with a current browser, spreadsheet editor and reliable internet.
WEEK-BY-WEEK CURRICULUM
Each week combines explanation, guided practice and evidence review across three sessions. The lab tasks and portfolio outputs below form the assessment trail.
GenAI, RAG and agents; data flows, shadow AI, critical assets, ownership and third-party dependencies.
Lab: Use AI to draft an inventory from a fictional company pack; verify every entry against source documents.
Evidence: AI inventory and data-flow record.
OWASP GenAI/agent risks, ATT&CK/ATLAS context and NIST CSF/AI RMF mapping.
Lab: Evaluate a security copilot and customer-facing AI agent; separate documented controls from assumptions.
Evidence: Cyber-risk register and source-linked control matrix.
Data handling, model provenance, hosting, subcontractors, access, retention and incident commitments.
Lab: Review a simulated vendor questionnaire and conflicting evidence; draft follow-up questions.
Evidence: Vendor assessment and residual-risk decision.
Least privilege, identity, approval rules, autonomy limits, monitoring, shutdown and AI-to-AI accountability.
Lab: Assess a proposed autonomous security workflow and set who may approve each action.
Evidence: Agent permission matrix and human-approval policy.
AI-assisted control testing, red-team evidence, false assurance, performance thresholds and exception handling.
Lab: Review a technical test report with missing evidence and inflated success claims.
Evidence: Assurance checklist, test acceptance criteria and exceptions log.
AI incident management, reporting ownership, records, legal-review triggers and jurisdiction-specific obligations. Framework mapping is not proof of legal compliance.
Lab: Run a tabletop involving a leaking AI assistant; build a defensible decision timeline.
Evidence: Incident protocol and management briefing.
Translate technical evidence into risk decisions; assess a newly published AI-security advisory.
Lab: Act as risk owner in a staged agent compromise and challenge unverified containment claims.
Evidence: Range decision log and emerging-threat control update.
Control ownership, evidence cadence, risk acceptance, metrics and a practical implementation roadmap.
Lab: Present a security governance pack for a simulated AI deployment and defend approval conditions.
Evidence: Board-ready portfolio, action plan and individual oral review.
| Coverage | Tools and material | Access and use |
|---|---|---|
| Hands-on core | Spreadsheet risk register · Approved AI assistant · NIST CSF / AI RMF · OWASP GenAI guidance · MITRE ATT&CK / ATLAS · Technical evidence packs | Create and verify risk, control and assurance records; no programming lab requirement. |
| Framework-based | OWASP GenAI and Agentic Security · MITRE ATT&CK/ATLAS · NIST CSF/AI RMF · NIST adversarial-ML taxonomy | Map threats and controls, justify test scope and trace evidence. These are references, not software subscriptions or AI5 accreditations. |
| Demonstrated or evidence-led | Technical red-team output, cloud security findings, agent traces and SOC incidents | Instructor walkthroughs or sanitized evidence packs; not every platform is a student lab. |
| Commercial / usage-dependent | Microsoft Security Copilot, Sentinel, Splunk, enterprise AI assistants, cloud accounts and paid model APIs | Hands-on access only when a suitable license, tenant and budget are confirmed. These subscriptions, credits and exams are not automatically included. Equivalent local exercises support the core learning goals. |
Use only approved AI endpoints with synthetic or sanitized data. Product names describe training context, not partnerships. Tool versions and the exact lab access list are confirmed for each cohort.
ASSESS THE WORK, NOT THE PROMPT
Deliver a cyber-governance approval pack for a simulated enterprise introducing GenAI and agents: inventory, risks, verified controls, vendor findings, incident duties, monitoring metrics, ownership and a 90-day action plan.
Reproducible results, source checks, tested controls and useful lab records.
Range performance, uncertainty, approval boundaries and communication.
Working or auditable deliverables, unseen test cases, handover and individual explanation.
Completion standard: 70% overall, all mandatory submissions and a pass on evidence verification and authorization controls. Unsafe unapproved actions or fabricated evidence must be corrected and reassessed. AI assistance must be disclosed; copied model output without verification is not accepted as proof.
Technical scorecards include false positives, detection or attack-success measures, clean-task performance and reproducibility. Governance scorecards check evidence completeness, control ownership, risk decisions and traceability. An attractive report alone does not meet the standard.
PRACTISE THE WHOLE DECISION
The Cyber Range is a sequence of isolated training scenarios using local applications, synthetic company records and replayed security events. Technical routes test controls; the governance route reviews the evidence and authorizes decisions in tabletop exercises.
Work with incomplete evidence, noisy alerts and an AI system that can make mistakes. Record hypotheses and competing explanations.
Reproduce findings, test benign controls and keep source references, timestamps, configuration and version records.
Request approval for changes, test containment and rollback, then explain remaining risk in an operational handover.
All testing stays within authorized training targets. No live third-party attacks or real customer secrets. Hosted range subscriptions and continuous access are not assumed; confirm the cohort’s delivery and access arrangements before enrolment.
BUILT TO KEEP LEARNING
Each cohort examines a recent AI-security advisory or research finding within its scheduled lab time. Learners check the source, assess relevance, reproduce a safe bounded example where feasible, test a mitigation and add an evidence-backed advisory to their portfolio.
Topics may include new agent protocols, AI-to-AI trust failures, memory poisoning, impersonation, autonomous tool misuse or changes in defensive models. This prepares learners for increasingly capable AI without speculative claims about AGI. The lab refreshes case material while preserving the program’s core learning outcomes and hours.
English-language live instruction, practical assignments and individual review. International learners should share their country and time zone so admissions can confirm a suitable cohort, local class times and daylight-saving changes before enrolment. Three sessions per week; generally 60–90 minutes each.
AI5 also serves learners in India and Delhi NCR. These programs are listed as Live Online; any on-site company delivery requires a separate agreed scope. Ask for batch dates, fees, applicable taxes, payment currency and international payment instructions. No batch date or commercial-tool access is promised until confirmed.
Companies can request a private cohort around approved tools, sanitized scenarios, team roles and measurable acceptance criteria. Discuss company training.
AI-assisted security work and the security of AI systems are the starting point. Every workflow requires evidence checks, explicit authority and measurable tests. Conventional foundations are prerequisites or targeted refreshers, rather than the main curriculum.
8 weeks × 3 sessions = 24 live sessions. At 60–90 minutes each, that is 24–36 instructor-led hours. Independent assignments are additional.
Basic understanding of business risk, security controls and evidence review. No coding prerequisite; technical control demonstrations are explained through business decisions and risk scenarios.
Do not assume a commercial subscription, cloud credit, vendor exam, external certification or CPE entitlement is included. Core practical work uses local/open tools and synthetic evidence where possible. Ask admissions for a written list of any batch-specific access costs and completion documentation.
Yes, through Live Online training in English, subject to a suitable confirmed cohort. Share your time zone and objectives. Companies may request a private cohort with agreed tools and sanitized scenarios.
No. The assessed output is a portfolio of verified workflows and control evidence. Hiring and production security depend on experience, the environment and ongoing review; a course or scanner cannot guarantee either.
CURRICULUM REFERENCES
Source pages checked September 2026. Each cohort records the editions, tool versions and advisories used in its lab briefs. Framework use does not imply accreditation, partnership or endorsement.
OWASP GenAI Security Project ↗
GenAI application risks and control guidance.
OWASP Agentic Security Initiative ↗
Agent trust boundaries, identity, tools and memory risks.
MITRE ATT&CK ↗
Threat behaviours, hunting hypotheses and detection coverage.
MITRE ATLAS ↗
Adversarial threats to AI systems.
NIST Cybersecurity Framework ↗
Cybersecurity outcomes and control ownership.
NIST AI Risk Management Framework ↗
AI risk management and evidence review.
NIST Adversarial Machine Learning taxonomy ↗
Attack and mitigation terminology.
Tell us your experience and time zone. We will use those details to discuss a suitable cohort and confirm access requirements.
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