AI Governance, Security & Compliance
Built in from day one — not bolted on after. Governance is what makes AI scalable.
Talk to an AI Governance SpecialistFour Pillars of Enterprise AI Governance
Our governance framework addresses the four domains that determine whether an enterprise AI program is defensible — to regulators, auditors, boards, and customers.
Risk Framework
Based on NIST AI RMF
Map, measure, and manage AI risk across your deployment portfolio using the NIST AI Risk Management Framework as the structural foundation. Every model, every workflow, and every integration is assessed for risk exposure — not as a one-time exercise but as an ongoing practice.
What this covers
- Risk categorization by workflow criticality and decision stakes
- Threat modeling for adversarial inputs and model failures
- Risk register with ownership, likelihood, and impact scoring
- Continuous monitoring cadence and escalation thresholds
Data Security & Privacy
From ingestion to inference
Every enterprise AI deployment creates new data exposure surfaces. We design data lineage, access controls, PII handling protocols, and model input/output logging to ensure your sensitive data stays under control — whether you are using third-party APIs or self-hosted models.
What this covers
- Data classification and sensitivity tagging for AI inputs
- PII detection and redaction in training data and prompts
- Vendor data handling review for all third-party model APIs
- Model input/output logging with retention and access controls
Human Oversight & Controls
Escalation paths for high-stakes decisions
Fully autonomous AI decisions are rarely appropriate at enterprise scale. We design escalation paths, approval workflows, and override mechanisms that keep humans appropriately in the loop — proportionate to the stakes of each specific workflow.
What this covers
- Human-in-the-loop design for high-stakes AI decisions
- Confidence thresholds that trigger human review automatically
- Override and correction mechanisms with audit trail
- Role-based approval workflows for consequential AI actions
Audit-Readiness
Documentation that satisfies regulators
Regulators, auditors, and customers will ask hard questions about your AI systems. We design the documentation, model cards, provenance tracking, and incident response protocols that let you answer those questions with confidence — not scramble to reconstruct records after the fact.
What this covers
- Model cards documenting capabilities, limitations, and intended use
- Decision provenance tracking for AI-influenced outcomes
- Incident response runbooks for model failures and data breaches
- Compliance mapping against relevant regulations (GDPR, CCPA, industry-specific)
What Governance-Ready Deployment Looks Like
Security and accountability are not optional at enterprise scale. Every model, every workflow, every integration needs clear ownership, clear controls, and a clear escalation path. A governance-ready AI deployment satisfies all eight of the following criteria before going into production.
Clear ownership: every AI system has a named accountable owner
Defined scope: every model has explicit boundaries on what decisions it may influence
Data controls: inputs and outputs are logged, classified, and access-controlled
Human checkpoints: escalation paths exist for all high-stakes decisions
Incident response: there is a playbook for model failures, data leaks, and hallucinations
Audit trail: every consequential AI output is traceable to its inputs
Change management: model updates go through a review and approval process
Ongoing monitoring: model performance and risk exposure are reviewed on a defined cadence
Most enterprise AI programs can check two or three of these on day one. The gap between where organizations start and where they need to be is exactly what our governance consulting closes — systematically, not through a 200-page policy document.
Talk to an AI Governance Specialist
Tell us about your current AI deployments, your regulatory environment, and your biggest governance concerns. We will identify the gaps and a practical path forward.
Book a Governance Consultation