AI security risk is escalating faster than organizations can measure it. AI governance frameworks such as ISO/IEC 42001 establish oversight and accountability, but they do not evaluate the security of AI systems. As AI becomes embedded in products, services, and vendor ecosystems, validated AI security assurance offers a stronger, more practical way to measure and reduce real AI risk, where governance alone falls short.

What is the difference between AI governance and AI security?

AI governance defines how AI is managed within an organization. It focuses on policies, decision-making structures, roles, and oversight intended to ensure responsible and compliant AI use.

AI security focuses on how AI systems are protected. It examines whether controls are implemented in deployed systems, whether they are tested, and whether they actually work.

Governance sets expectations. Security assurance validates reality.

Why AI security risk remains largely invisible in TPRM

AI is being deployed at a pace that outstrips traditional risk management models. Vendors are introducing AI capabilities continuously, often without clear visibility into how those systems are secured or monitored.

Most third-party risk programs still rely on indirect signals such as questionnaires and attestations to assess vendor security.

With AI, this approach breaks down. Risk teams are left to infer security posture from narrative evidence, while the actual AI systems remain untested. Because AI security controls are selectively tested and rarely validated, organizations often do not know what protections are actually in place until an incident occurs. This creates a false sense of control, where risk appears managed on paper but remains unmeasured in practice.

Why governance frameworks cannot reduce AI security risk

Governance frameworks are designed to manage behavior, not validate technical outcomes.

They do not

• Prescriptively define AI security controls
• Require testing of deployed AI environments
• Validate control effectiveness through independent assessment
• Provide standardized, comparable evidence of AI security

For instance, ISO/IEC 42001 is a governance framework designed to help organizations establish an AI Management System (AIMS). It provides structure around accountability, documentation, and continuous improvement for AI activities. However, ISO/IEC 42001 does not deeply assess the security of AI systems that are deployed and in use. Controls may be selectively implemented and selectively tested by accredited as well as unaccredited certification bodies, resulting in inconsistent assurance strength.

How AI security assurance delivers stronger risk reduction

AI security assurance focuses on measurable outcomes.

Rather than evaluating intent, it validates whether security controls are implemented, tested, and effective in real AI systems. This provides clear evidence that AI-related threats are being addressed.

Unlike management system audits, effective AI security assurance requires that all applicable controls be tested, using consistent methods and rigor through authorized assessors, so results can be relied upon by regulators, customers, and third-party risk teams.

The HITRUST AI Security Assessment and Certification was built specifically to deliver this level of assurance.

How HITRUST AI compares to ISO/IEC 42001

For a detailed comparison between HITRUST AI Security Assessment and Certification and ISO/IEC 42001, read our recent blog post.

Why HITRUST offers what governance frameworks cannot

HITRUST is the only reliable solution built for AI security assurance. HITRUST AI Security Certification provides organizations with something governance frameworks are not designed to deliver: trusted proof of AI system security.

It is

• Fast, enabling timely response to emerging AI threats
• Focused, targeting real AI security risks in deployed systems
• Affordable, allowing assurance to scale across vendors and internal environments

This makes AI security assurance practical, actionable, and repeatable.

HITRUST also delivers consistent assurance through vetted assessors, prescriptive, threat-driven testing requirements, and centralized quality assurance, reducing the variability and interpretation risk common in governance-based certifications.

What this means for organizations managing AI risk

AI governance establishes expectations. AI security assurance establishes trust.

As AI continues to permeate vendor ecosystems, organizations cannot rely on oversight alone. They must be able to measure security directly and act on verified results.

The organizations that move first will not just respond to AI risk — they will control it.