Service / AI System Advisory & Development

AI Red Teaming & Scenario Planning Exercises

Exercise technical and executive teams against realistic AI failure, misuse and incident scenarios before they occur in production.

Business context

What this capability solves

Organizations need to rehearse not only attacks but decisions: whether to shut down an agent, override a model, notify stakeholders, roll back a release or accept residual risk.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Scenario Design

Model failure, data leak, prompt injection, poisoning, agent misuse, deepfake and regulatory event scenarios.

Technical Injects

Logs, prompts, alerts, tool traces, data artifacts and architecture evidence.

Decision Injects

Customer harm, regulator inquiry, media escalation, vendor issue and executive trade-offs.

Adaptive Facilitation

Branch scenario based on participant decisions and control maturity.

Scoring

Decision quality, response time, evidence, control use and communication.

After-Action Review

Root causes, policy/process gaps and prioritized remediation.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Business objectives, accountable owners, risk appetite, policy, use-case portfolio and investment priorities.

AI Engineering Lifecycle

Data, model, prompt/RAG, agent, evaluation, release, monitoring and retirement controls.

Trust & Assurance

Risk/impact assessment, security, privacy, explainability, human oversight, testing and evidence.

Enterprise Operations

Integration, observability, incident handling, change governance, model/agent lifecycle and continuous improvement.

Controls & governance

Named business and technical owner
Use-case risk classification and approval gates
Data provenance, minimization and access control
Human accountability for high-impact outcomes
Security and privacy-by-design controls
Versioned model/prompt/agent configuration
Pre-release evaluation and red-team gates
Continuous monitoring, incident and change control
Audit-ready evidence and management reporting

Priority use cases

AI incident TTX
Agentic AI crisis exercise
GenAI data leakage scenario
Deepfake/BEC exercise
AI vendor failure
Regulatory response exercise

Key deliverables

Scenario pack
Inject library
Facilitation guide
Decision log
Scorecard
After-action report
Improvement backlog

Integration considerations

Enterprise IAM and workload identity
Data lake/warehouse and vector/RAG platforms
Model/API providers and private models
Application/API integration layer
MLOps/LLMOps/AgentOps and observability
SIEM/SOAR and security tooling
GRC, privacy and evidence repositories
ITSM/BPM and business workflow systems

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. AssessInventory use cases, systems, stakeholders, data, risks, maturity and constraints.

2. DesignDefine target architecture, governance, controls, delivery backlog and acceptance criteria.

3. BuildDevelop or configure AI capabilities, integrations, controls, evaluation and operating procedures.

4. AssureTest quality, safety, security, privacy, explainability and business acceptance before release.

5. OperateMonitor outcomes, drift, incidents, changes, cost, risk and control effectiveness through BAU governance.

Outcome and KPI framework

Decision latencyEscalation accuracyControl-use effectivenessEvidence completenessAction closureRepeat exercise improvement