Service / AI System Advisory & Development

AI Red Teaming & Adversarial Testing

Authorized testing of AI systems against misuse, prompt injection, jailbreaks, data leakage, retrieval manipulation, tool abuse and autonomous failure modes.

Business context

What this capability solves

AI systems can fail under adversarial interaction even when conventional application controls are sound. Structured red teaming validates actual behavior, not policy assumptions.

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.

Threat Scenario Design

Define abuse cases by model, RAG, agent, tools, data and business impact.

Prompt / Jailbreak Testing

Test instruction hierarchy, policy bypass, obfuscation, multi-turn steering and unsafe completion.

RAG / Data Leakage

Test retrieval authorization, cross-tenant leakage, indirect injection and sensitive-data reproduction.

Agent / Tool Abuse

Test excessive agency, privilege escalation, transaction boundary and tool-chain manipulation.

Model / Supply Chain

Probe unsafe dependencies, model artifacts, insecure loaders and provider assumptions.

Control Regression

Convert findings into repeatable tests for future releases.

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

Pre-production AI assurance
High-risk GenAI application
Agentic workflow validation
RAG security test
Model/provider change
Post-incident retest

Key deliverables

Red-team plan and RoE
Attack scenario library
Evidence and findings
Risk-ranked remediation
Regression test pack
Executive technical report

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

Attack scenario coverageCritical exploitabilityRemediation closureRegression pass rateUnauthorized action preventionSensitive-output rate