AI security certification has become a critical requirement for organizations assessing the risk posture of AI-enabled vendors, especially as AI becomes deeply embedded in sensitive and regulated business processes. For third-party risk management (TPRM) teams, the question is no longer whether vendors use AI, but whether those AI systems are secure, governed, and independently validated against recognized security standards.
As AI adoption accelerates across industries, traditional vendor risk assessments are struggling to keep pace. Questionnaires and attestations alone cannot adequately address the unique risks introduced by AI models, training data, and automated decision-making. This is where AI security certification plays a pivotal role, providing structured, testable assurance that AI systems meet defined security and risk management expectations.
Third-party vendors increasingly rely on AI to process protected health information (PHI), financial data, intellectual property, and other sensitive assets. From clinical decision support tools to fraud detection engines, AI systems now sit directly in the flow of regulated data.
For organizations managing third-party risk, this creates a new exposure layer. A vendor’s AI model may introduce risks that extend far beyond traditional infrastructure or application security, making AI security assessment an essential component of modern vendor due diligence.
AI systems present distinct security challenges that are often overlooked in standard risk assessments. These include risks related to training data integrity, model drift, prompt injection, adversarial attacks, and unintended data leakage through model outputs. Vendors may also struggle to demonstrate consistent governance over how AI systems are developed, deployed, and monitored over time.
Without a recognized AI assurance framework, organizations are left to interpret vendor claims without objective validation, an approach that increases uncertainty and risk.
Uncertified AI deployments can expose organizations to regulatory scrutiny, operational disruptions, reputational damage, and downstream third-party failures. For TPRM leaders, the absence of AI security certification complicates vendor onboarding, slows procurement, and increases residual risk across the supply chain.
Independent assessment helps reduce these challenges by offering a standardized, repeatable way to evaluate AI security controls at scale.
AI security certification is a formal, independent proof of evaluation of an AI system’s security, governance, and risk management controls. Unlike high-level ethical AI principles or self-attested compliance checklists, certification focuses on whether AI systems are implemented and operated securely in real-world environments.
For third-party risk teams, AI security certification serves as objective evidence that a vendor’s AI system has been assessed against defined security requirements.
A certifiable AI security program typically includes controls for data protection, secure model development, access management, monitoring, incident response, and governance oversight. It also requires documented policies, repeatable processes, and demonstrable implementation — elements that are critical for scalable vendor risk management.
HITRUST brings these components together through a structured, security-first approach to AI assurance.
AI security assessment helps organizations validate that vendors have safeguards in place to protect training data, prevent unauthorized model access, and detect tampering or degradation over time. This is particularly important when vendors rely on large datasets sourced from multiple environments.
TPRM teams gain confidence through certification that AI systems are not only functional but resilient against integrity and availability risks.
Adversarial attacks, model manipulation, and unintended bias pose significant risks to organizations relying on third-party AI. Certification frameworks designed for AI security evaluate whether vendors have controls to identify, mitigate, and respond to these threats.
By requiring certified assurance, organizations can reduce the likelihood that vendor AI systems introduce compliance, ethical, or operational failures into their ecosystems.
The HITRUST AI Security Assessment and Certification was designed to solve a specific and increasingly urgent problem for organizations and third-party risk management teams: proving that deployed AI systems are secure.
Rather than focusing on high-level governance maturity or policy intent, HITRUST evaluates AI-specific security risks in real, operational environments. The assessment applies prescriptive, threat-mapped controls tailored to how and where AI is deployed, ensuring that security requirements align directly to practical risk scenarios.
Independent testing, centralized quality assurance, and formal HITRUST certification together deliver defensible, evidence-based AI security assurance that TPRM teams can rely on across vendor ecosystems.
HITRUST’s AI security assessment aligns with and maps to leading global standards and guidance, including NIST publications, ISO/IEC standards, and OWASP resources. However, HITRUST differs materially from governance-first approaches such as ISO/IEC 42001. HITRUST provides prescriptive security requirements and standardized assurance outcomes.
Developed through extensive industry collaboration, HITRUST AI Security Certification enables scalable trust across regulated industries where AI risk is embedded in third-party products and services. The assessment includes 44 harmonized, AI-specific security controls with explicit mappings between threats and required safeguards, and it is regularly updated to address emerging AI risks.
In healthcare, HITRUST-certified AI systems support the protection of PHI and regulatory compliance. In financial services, they help organizations validate the security of AI-driven analytics, automation, and fraud detection. Across industries, standardized reporting supports executives, regulators, and TPRM teams alike.
By certifying systems and environments, HITRUST delivers clear proof that AI systems are protected, enabling organizations to make confident, defensible third-party risk decisions at scale.
Vendors typically begin by evaluating their AI systems against HITRUST requirements to identify gaps. Additional insights on building trust in AI highlight how structured assurance accelerates confidence across stakeholders.
Certification requires validation by an authorized HITRUST Assessor, ensuring independence and consistency. This third-party validation is a key differentiator for risk teams seeking defensible assurance outcomes.
Organizations exploring broader assurance options can also review HITRUST’s full portfolio of assessments and certifications to support holistic risk management strategies.
AI risk does not remain static. HITRUST emphasizes ongoing monitoring and reassessment to ensure certified AI systems continue to meet security expectations as models evolve and threats change. This approach supports efficient AI risk management across the vendor lifecycle.
For organizations managing third-party risk, AI security assessment is foundational to maintaining trust, resilience, and compliance in an AI-driven ecosystem. By leveraging HITRUST’s structured, scalable pathways, organizations can gain defensible, repeatable AI assurance.
Secure AI systems with confidence and explore HITRUST’s proven path to AI security certification and risk reduction.