DefineEstablish rules and boundaries
The foundational rules defining how AI systems must be built, deployed, and operated. Acceptable use policies, model approval standards, data handling requirements, and security baselines. Without this layer, everything else is enforcement without law.
Who can interact with AI systems, at what level, and under what conditions. Authentication, role-based permissions, API key management, SSO integration, directory sync, and privileged access management.
Controls around what data AI systems can see, use, store, and transmit. Data classification, input/output filtering, PII detection, data residency requirements, and ensuring models don't inadvertently exfiltrate sensitive information through outputs.
Oversight of the AI models themselves — provenance, vetting, versioning, and change approval. Includes third-party model risk management (OpenAI, Anthropic, open-source) and ensuring behavioral consistency over time.
EnforceApply controls at runtime
Active guardrails during AI operation: rate limiting, session controls, topic restrictions, and content moderation. Controls are the real-time "if-then" enforcement layer — where policy becomes action.
Oversight of external AI providers, APIs, and tools. Covers vendor security assessments, contractual data handling requirements, model change notification expectations, and contingency planning for vendor-side disruptions.
Pre-execution inspection of all inputs to AI systems. Detects and blocks prompt injection attempts, jailbreak patterns, sensitive data in inputs, and out-of-scope instructions before they reach the model.
Post-generation inspection of all AI outputs before they reach the user. Catches PII leakage, harmful content, hallucinated credentials, and policy-violating responses. The last line of defense before output delivery.
Structured tracking of all changes to models, prompts, configurations, and integrations. Ensures every production state is reproducible, auditable, and reversible. Critical for diagnosing behavior changes and demonstrating control over AI system evolution.
MonitorSee everything in real time
Real-time visibility into AI system health, usage, and performance. Tracks model latency, error rates, throughput, and resource consumption. Observability is what makes governance enforceable — without visibility, you can't prove compliance or catch violations.
End-to-end lineage tracking for every AI decision — from input to output, linking model version, prompt, context, and user. Enables root cause analysis, regulatory evidence, and the ability to reconstruct any AI interaction on demand.
Persistent, tamper-resistant records of all user–AI interactions. Captures prompts, responses, timestamps, session metadata, and user identity. The primary input for compliance audits, incident investigations, and behavioral pattern analysis.
Automated identification of unusual patterns in AI system usage — spikes in sensitive queries, out-of-hours access, repeated policy violations, and model behavior drift. Surfaces risks that rule-based controls miss.
Measurement of how AI systems are actually used — by whom, how often, for what purposes, and at what cost. Informs capacity planning, license compliance, ROI analysis, and identifies under- or over-utilised deployments.
RespondAct when things go wrong
Structured review of AI system behavior against policy — for internal assurance or external regulations (SOC 2, ISO 27001, EU AI Act, NIST AI RMF). Audit turns observability data into accountability and evidence.
The playbook for when things go wrong: prompt injection, data leakage through model output, rogue deployments, unexpected behavior. Defines detection, escalation, containment, investigation, and reporting protocols.
Identifying, assessing, and mitigating risks introduced by AI: operational (model failure), reputational (harmful outputs), compliance (regulatory exposure), and third-party (vendor dependency). Connects AI governance to enterprise risk frameworks.
Automated production of compliance artifacts — audit reports, control attestations, policy adherence summaries, and regulatory submissions. Transforms raw monitoring data into structured, auditor-ready documentation.
SustainMaintain governance over time
Clear assignment of responsibility at every layer — model, deployment, data pipeline, security controls. Defines AI system owners, establishes review and approval chains, and ensures a human is in the loop for high-stakes decisions. Governance without accountability is just documentation.
Ongoing analysis of AI adoption patterns, utilisation rates, and business outcomes across the organisation. Provides the data needed to justify investment, identify gaps, and demonstrate governance maturity to stakeholders and regulators.
Controls around how AI systems evolve — new model versions, prompt changes, configuration updates, integrations. Like traditional software change management but applied to AI, where small changes can produce dramatically different behavior. All changes reviewed, tested, approved, and logged before production.
Ongoing adherence to contractual, regulatory, and acceptable-use obligations governing AI deployments. Tracks license terms, usage restrictions, data processing agreements, and regulatory deadlines to ensure sustained legal and policy compliance.