IT Security
AI Red Teaming and Adversarial Testing
AI Red Teaming and Adversarial Testing helps organizations reduce exploitable attack paths while preserving workable operations. Participants examine threat modeling for AI, attack-plan design, and jailbreak and extraction testing before producing a risk-prioritized security design, playbook, or investigation record.
Overview
Practical learning for workplace transfer.
The course is designed around the decisions practitioners actually face in AI Red Teaming and Adversarial Testing. Its progression—from threat modeling for AI through evidence and severity—uses threat-led analysis, control testing, and realistic defensive scenarios. The final remediation retest exercise requires participants to justify recommendations, test assumptions, and set practical next steps.
Objectives
- Explain the role, scope, and business significance of threat modeling for AI in helping organizations reduce exploitable attack paths while preserving workable operations.
- Diagnose attack-plan design through threat-led analysis, control testing, and realistic defensive scenarios and prioritize the most material gaps.
- Design an approach to jailbreak and extraction testing with the roles, safeguards, dependencies, and evidence needed to produce a risk-prioritized security design, playbook, or investigation record.
- Evaluate evidence and severity using measures and failure scenarios appropriate to threat-led analysis, control testing, and realistic defensive scenarios.
- Complete the remediation retest exercise and translate its findings into owned actions leading toward a risk-prioritized security design, playbook, or investigation record.
Target audience
- Security architects, engineers, analysts, and SOC personnel
- CISOs, cyber-risk leaders, and incident coordinators
- Application, identity, infrastructure, cloud, and OT teams
- Audit, assurance, resilience, and technology managers
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: threat modeling for AI
Establish the vocabulary, boundaries, stakeholders, and decision context for threat modeling for AI.
Separate established requirements and reliable evidence from untested assumptions about threat modeling for AI.
Map threat modeling for AI to the organization’s current responsibilities, dependencies, and constraints.
Module 2: attack-plan design
Diagnose the current state of attack-plan design using a structured control validation lab.
Compare alternative methods and select an approach suited to risk, maturity, and scale.
Document requirements, owners, decision criteria, and exceptions for attack-plan design.
Module 3: jailbreak and extraction testing
Design the workflow, safeguards, and handoffs required for jailbreak and extraction testing.
Test normal, failure, and edge-case scenarios before operational adoption.
Review the design for security, quality, accessibility, sustainability, or assurance implications as relevant.
Module 4: evidence and severity
Define meaningful measures, evidence, review cadence, and escalation thresholds for evidence and severity.
Investigate performance gaps and separate root causes from symptoms.
Plan corrective action, controlled change, and accountable follow-through.
Module 5: Applied AI Red Teaming and Adversarial Testing Workshop
Complete a control validation lab that integrates the course decisions around remediation retest exercise.
Defend recommendations against a realistic stakeholder challenge or scenario.
Produce a prioritized workplace action plan with owners, dependencies, and review points.
Materials provided
- Course workbook and subject reference guide
- Applied scenarios, worksheets, and decision templates
- Implementation checklist or roadmap canvas
- 4D Certificate of Completion
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
The 4D approach to AI Red Teaming and Adversarial Testing begins with the client’s current decisions and constraints, then uses tailored cases to produce a risk-prioritized security design, playbook, or investigation record. Participants receive facilitated challenge and peer review so the output can support real follow-through. No external accreditation, legal opinion, or automatic compliance outcome is represented.
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