AI Risk & Impact Assessment
Assess technical, legal, ethical, privacy, security, operational and human impacts of AI use cases using a structured risk-based method.
What this capability solves
High-impact AI decisions can affect people, customers, employees, operations and public trust. A single technical risk score is insufficient; impact must be assessed across the socio-technical system.
Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.
Capability model
Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.
Use-Case Classification
Purpose, stakeholders, decision role, autonomy, scale, affected persons and criticality.
Data & Privacy
Lawful processing, minimization, sensitive data, profiling, inference and memorization risks.
Fairness / Human Impact
Disparate impact, accessibility, contestability and vulnerable groups.
Security / Safety
Adversarial risk, misuse, failure modes and safe operating boundaries.
Explainability / Oversight
Decision traceability, reason codes, meaningful human review and escalation.
Residual Risk Decision
Mitigation, owner, acceptance authority, monitoring and reassessment triggers.
How the capability fits together
Final topology, control placement and deployment model are validated during discovery and detailed design.
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
- High-impact automated decision
- Employee AI system
- Customer scoring/recommendation
- Generative AI product
- AI surveillance/biometrics
- Agentic workflow
Key deliverables
- AI impact assessment
- Risk register
- Control plan
- Residual-risk decision
- Human oversight design
- Monitoring requirements
- Management summary
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
Phased delivery
Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.