Harmonization of Compliance Frameworks

HITRUST AI RMF aligns your compliance pursuits with multiple frameworks, each with its own requirements, like NIST, ISO 42001, OWASP, GDPR, and more. Instead of treating them as separate checkboxes, HITRUST AI RMF takes a harmonized approach by aligning with the key principles of these standards within a single, structured framework.

It’s a practical, efficient way to manage AI security risks, reduce complexity, and ensure that nothing slips through the cracks.

So how does this work? Let’s take a look below:

HITRUST Incorporates Multiple Standards for AI Security

One of its strongest features is its ability to incorporate multiple established security and risk management frameworks into its structure. 

This approach ensures that organizations using HITRUST for AI security and risk management benefit from a broad and internationally recognized set of best practices.

These include:

• ISO/IEC 23894. AI-specific security and risk management principles. HITRUST AI RM, derived from ISO 23894, emphasizes AI security by addressing vulnerabilities in AI models.
• NIST AI Risk Management Framework (AI RMF). HITRUST AI RMF aligns with NIST AI RMF’s four core functions, Govern, Map, Measure, and Manage, by embedding continuous monitoring, bias detection, and explainability requirements.
• ISO/IEC 27001 & 27701. HITRUST integrates ISO 27001’s risk-based approach to managing security across AI systems and aligns with ISO 27701’s privacy governance to ensure AI models comply with data protection best practices.
• NIST Cybersecurity Framework (CSF). HITRUST AI RMF applies NIST CSF’s Identify, Protect, Detect, Respond, Recover approach to AI security, ensuring AI-driven applications follow strong access controls, data protection mechanisms, and cyber resilience protocols.
• SOC 2 & HIPAA. HITRUST AI RMF aligns with SOC 2’s Trust Services Criteria to address security, availability, and confidentiality in AI applications.

Practical Implementation

One of the biggest challenges with AI security is figuring out how actually to do it. That’s where HITRUST stands out. Instead of vague guidelines that leave organizations guessing, HITRUST lays out prescriptive controls that are clear, actionable, and easy to implement.

Here’s how it makes AI security more practical:

• HITRUST provides step-by-step guidance on protecting data, monitoring threats, and managing risks.
• Mapped controls for multiple standards. HITRUST integrates all of the key requirements into a single framework. This means you can address multiple compliance needs at once without duplicating efforts.
• Scalability for different AI applications. HITRUST offers controls that adapt to your industry and risk level, so you’re not stuck applying generic rules that don’t fit your needs.
• Risk-based approach. HITRUST helps organizations prioritize AI security measures based on actual risk rather than a one-size-fits-all checklist. That way, resources go where they’re needed most instead of being wasted on low-risk areas.
• Continuous monitoring and improvement. HITRUST makes tracking, measuring, and updating controls easier as threats evolve, ensuring organizations stay ahead of emerging risks rather than reacting too late.

AI-Specific Focus

Most security frameworks weren’t built with AI; they focused on general cybersecurity, data protection, and risk management. However, AI has its own challenges that require a more tailored approach, and that’s exactly where HITRUST’s AI-specific focus makes a difference.

Instead of just repurposing standard security controls, HITRUST customizes its framework to tackle the risks unique to AI, such as:

• Data bias & fairness. AI models are only as good as the data they’re trained on, and if that data is biased, the AI’s decisions will be too. For example, HITRUST integrates bias detection and mitigation controls, ensuring organizations use diverse and representative datasets during training.
• Model explainability and transparency. Another criticism of AI is that its decisions can be a black box; even developers don’t always know why a model reaches an inevitable conclusion. HITRUST introduces governance controls that require precise documentation of AI models, logic, decision-making processes, and explainable AI (XAI) techniques so businesses and regulators can verify AI outputs.
• Algorithmic security and manipulation risks. AI systems can be manipulated through adversarial attacks, where bad actors tweak input data to mislead AI models. HITRUST addresses this by requiring robust adversarial testing to see how AI reacts to deceptive inputs. It also supports defensive mechanisms like adversarial training to harden AI models against manipulation.