AI Red Teaming and Adversarial Testing

Course Category : Risk Management

An advanced programme for systematically stress-testing AI systems, identifying vulnerabilities and safety risks, and evaluating safeguards before weaknesses escalate into operational or security exposure.
Duration: 5 Days
Level: Advanced

Introduction

The assurance of AI systems increasingly depends on an organisation's ability to examine their behaviour beyond normal operating conditions rather than relying solely on accuracy or technical performance. Generative and intelligent systems can be exposed to manipulated inputs, safeguard circumvention, unintended information disclosure, and outputs that conflict with established security and safety requirements.
This course addresses AI Red Teaming and adversarial testing through a structured, risk-based perspective. It covers attack-surface identification, test scenario design, findings classification, safeguard evaluation, remediation governance, and retesting to strengthen the security, resilience, and trustworthiness of AI systems..

Targeted Audience

AI Security and Cybersecurity Professionals
Red Team, Blue Team, and Security Testing Professionals
Generative AI Engineers and Specialists
AI Governance and Risk Professionals
Information Security and Technology Risk Officers
AI Assurance and Quality Professionals
Technology, Digital Transformation, and AI Managers
Compliance and Technology Control Professionals

Targeted Skills

Risk-Based AI Red Team Planning
AI Attack Surface and Vulnerability Analysis
Adversarial Testing and Input Manipulation Assessment
Safeguard Circumvention Resistance Evaluation
Risk-Based Findings Classification
Guardrail and Safety Control Evaluation
AI Red Team Reporting
Remediation, Retesting, and Continuous Improvement

Expected Outcomes

Explain the role of AI Red Teaming within AI security and governance.
Identify attack surfaces and relevant adversarial scenarios across AI systems.
Develop risk-based adversarial testing plans based on assets, threats, and exposure.
Assess prompt and input manipulation risks and attempts to circumvent model safeguards.
Evaluate the effectiveness of guardrails, controls, and monitoring mechanisms.
Classify testing findings according to security and operational impact.
Structure AI Red Team reports around evidence, risk, and remediation priorities.
Establish retesting and remediation-validation methodologies.

Training Topics Index

AI Red Teaming concepts, objectives, and relationship with conventional security testing
Threat characteristics of generative AI and intelligent systems
Identification of critical assets, trust boundaries, and system boundaries
AI attack-surface analysis and potential misuse pathways
Threat scenario development and risk-based prioritisation

Adversarial testing principles and model robustness assessment
Prompt injection and instruction or context manipulation risks
Analysis of attempts to circumvent safeguards and restrictions
Sensitive information and unintended disclosure risks
Evaluation of unsafe behaviours and undesirable model outputs

Test scope, objectives, and success or failure criteria
Test case development and adversarial scenario libraries
Rules of engagement, boundaries, and testing controls
Systematic documentation of observations, evidence, and findings
Assessment of finding severity, exploitability, and organisational impact

Analysis of model protection layers and surrounding controls
Input, output, and content-control assessment
Robustness evaluation against adversarial input patterns
Monitoring, logging, and anomalous-behaviour detection mechanisms
Control effectiveness measurement and residual-gap identification

Risk-based classification and prioritisation of vulnerabilities and findings
Root-cause analysis and mapping findings to organisational controls
Remediation and risk-mitigation requirements
AI Red Team reporting for management and technical stakeholders
Retesting, remediation validation, and continuous improvement

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

AI Red Teaming and Adversarial Testing