They start from the same inventory
Neither team can act on AI it cannot see. Both need the same discovery and the same AI‑BOM.
What is an AI-BOM?
They started as separate disciplines with separate owners. For enterprise AI, they now depend on the same inventory, the same evidence and the same loop.
AI governance sets the policies, accountability and compliance controls that decide how AI should be used.
AI security protects AI systems, their data and their users from attack, misuse and failure. Governance defines what “safe and compliant” means; security makes sure it holds under pressure. For enterprise AI the two are converging, because neither works without the other.
| AI governance | AI security | |
|---|---|---|
| Core question | Should this AI be used, and how? | Can this AI be attacked, misused or broken? |
| Typical owner | Governance, risk, compliance and legal | CISO, security and AppSec teams |
| Key activities | Policy, risk classification, framework mapping, approvals | Red teaming, threat detection, runtime protection |
| Frameworks | EU AI Act, NIST AI RMF, ISO 42001 | OWASP Top 10 for LLMs, adversarial testing |
| Output | Evidence of compliance | Evidence of resilience |
In practice, every governance decision depends on security facts, and every security finding becomes a governance question.
Neither team can act on AI it cannot see. Both need the same discovery and the same AI‑BOM.
What is an AI-BOM?A governance rule that cannot be enforced at runtime is a document, not a control.
Red-team results and runtime evidence are exactly what auditors and regulators ask governance teams for.
Autonomous agents take actions, so a policy failure and a security failure become the same incident.
Boards and customers ask one question, whether the AI is trustworthy, and expect one answer.
Third-party AI needs both a governance decision and a security assessment before it is approved.
Third-party AI riskCranium AI built Cranium ONE as a single AI security and governance platform, organized around five continuous moves.
Find every model, agent, dataset and AI vendor, including shadow AI, and record it in AI‑BOMs.
Watch live model and agent activity to catch drift and anomalies early.
Turn policy into live controls mapped to the EU AI Act, NIST AI RMF and ISO 42001.
Red team models and agents with Arena, using human and AI-driven attacks.
Share framework-mapped evidence on demand through Cranium AI Cards and Trust Hubs.
Straight answers, kept current. Last updated .
Ask us anything →AI governance sets the policies, accountability and compliance controls for how AI is used. AI security protects AI systems and their data from attack, misuse and failure.
Yes. Both depend on the same inventory of AI, governance policies need security controls to be enforced, and security findings become the evidence governance teams need for compliance.
Governance usually sits with risk, compliance and legal, while security sits with the CISO. As they converge, enterprises increasingly run both on a shared platform with shared evidence.
Not necessarily. Separate tools mean separate inventories and evidence that does not line up. Cranium ONE is one platform for both, built around the AI Trust Loop.
Governance commonly maps to the EU AI Act, NIST AI RMF and ISO/IEC 42001. Security teams often add the OWASP Top 10 for LLM applications and adversarial testing.
It is Cranium AI’s five-move model for trustworthy AI: Discover, Observe, Govern, Secure and Prove, run continuously across every model, agent and vendor.
See how Cranium AI discovers, observes, governs, secures and proves every model, agent and vendor in your enterprise.
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