Policy & accountability
Define ownership, risk tiers, approval routes, human oversight and evidence requirements.
Cybersecurity / AI security
Govern intelligent systems before they govern decisions.
Responsible AISecurity by design
Control risk across models, data, agents and integrations without preventing valuable AI adoption.
Review your AI risk ↗A connected discipline
Responsible AI and AI security are inseparable. Governance defines what should happen; assurance proves whether the system behaves accordingly.
Control risk across the AI lifecycle.
From first use-case decisions to production monitoring, controls remain proportionate to impact and autonomy.
AI threat assessment
Evaluate models, data, prompts, agents, tools and integrations against credible misuse.
Testing & monitoring
Validate behaviour, guardrails and security before deployment and throughout operation.
From inventory to continuous assurance.
A living governance system follows AI as its context, capability and exposure evolve.
- 01Discover
Inventory systems, owners, data, models, integrations and intended outcomes.
- 02Classify
Assess impact, autonomy, exposure and applicable obligations.
- 03Control
Design safeguards, approval gates, oversight and secure operating boundaries.
- 04Assure
Test behaviour, exceptions and control effectiveness with durable evidence.
What governed adoption enables.
Known AI exposure
Leaders can see which systems exist, what they affect and who owns the risk.
Proportionate safeguards
Controls match the consequence and autonomy of each use case.
Evidence for decisions
Deployment and expansion decisions are supported by tested behaviour.