How Notable Turned HITRUST Certification into a Business Enabler

For healthcare technology companies, security is more than a technical requirement. It is a condition of trust.

That challenge becomes even more complex for organizations using artificial intelligence. As AI introduces emerging risks related to model integrity, training data security, and incident monitoring, companies must do more than say their technology is secure. They need a credible way to demonstrate it.

For Notable, pursuing both HITRUST r2 Certification and HITRUST AI Security Certification provided that assurance.

Building trust in a rapidly evolving AI environment

Notable automates more than 1.4 million tasks each day for major health systems, medical groups, and payers and has served more than 32 million patients. Its platform handles work that healthcare organizations have traditionally managed manually.

Because the platform operates in healthcare and uses AI, security has always been a priority. The challenge was demonstrating that commitment in a way customers could trust.

“Before we had our HITRUST certification, it was difficult to reassure our customers about our security posture,” said Vivian Lee, Security Analyst at Notable. “We needed some way to show them that we take security seriously.”

Notable selected HITRUST r2 Certification because of its reputation, rigor, and broad adoption across healthcare. The HITRUST CSF brings together more than 70 authoritative sources, including NIST, FedRAMP, ISO, and HIPAA, within a single framework.

When HITRUST introduced its AI Security Certification, Notable moved quickly to extend that assurance to the risks associated with its AI systems.

Validating enterprise security and AI-specific controls

The two certifications serve complementary purposes.

HITRUST r2 Certification validates Notable’s broader security and compliance posture. HITRUST AI Security Certification provides additional assurance that its AI systems are hardened against risks unique to AI environments, including model integrity, training data security, and incident monitoring.

“HITRUST r2 certification proves our overall security and compliance posture, while the HITRUST AI Security Certification proves we’ve hardened our AI systems against unique threats,” Lee said. “Both show that our platform and AI usage are validated to a high bar.”

Together, the certifications give customers stronger assurance that Notable’s platform, data practices, and use of AI have been independently assessed against a rigorous standard.

Rather than relying on internal claims or self-scoped assessments, Notable’s controls were reviewed through a structured process involving a HITRUST Authorized External Assessor and multiple layers of expert validation.

Reducing friction in customer and sales conversations

For Notable, the value of certification extends beyond the security organization.

The company has experienced several business benefits:

• Lower procurement barriers with healthcare organizations that require HITRUST certification

• Stronger competitive differentiation in the healthcare AI market

• Greater customer confidence that patient data is protected

• Faster movement through RFPs and sales conversations

“Sometimes, informing a prospective customer that we have a HITRUST certification helps accelerate sales conversation,” Lee explained.

This is where assurance becomes more than a compliance exercise. It becomes a practical business asset that helps establish credibility, satisfy customer requirements, and keep opportunities moving forward.

Turning security into a strategic asset

The certifications have also changed how security is viewed within Notable.

“Our experience with HITRUST r2 certification and HITRUST AI Security Certification has been very rewarding,” Lee said. “It has changed security from a cost center to a partnership enabler.”

By pursuing both certifications, Notable strengthened its security posture while creating a meaningful trust advantage in the healthcare AI market. The certifications provide customers with added confidence that Notable’s platform and AI systems have been validated to a high standard.

Read the full Notable customer story to learn how HITRUST r2 and AI Security Certifications helped the company strengthen customer trust, reduce procurement barriers, and support growth.

How AI Security Certification Helps Vendors Build Trust

An AI vendor risk assessment becomes more useful when vendors can provide independently validated evidence that their deployed AI systems have been evaluated against AI-specific cybersecurity expectations.

As artificial intelligence becomes embedded in products, platforms, workflows, and services, enterprise customers are asking more detailed questions about the technologies entering their environments. Vendors are no longer evaluated only on what their AI can do. They are also evaluated on whether their AI systems protect sensitive data, operate securely, and address the risks that matter to customers.

For vendors, AI security certification can provide a clearer way to answer those questions. It can turn trust from a claim into evidence that buyers can review and rely on throughout vendor evaluation and procurement.

The New Reality: AI Has Changed How Vendors Are Evaluated

AI is quickly becoming part of the enterprise vendor ecosystem.

It may support customer service, analytics, security operations, software development, automation, document processing, or business-critical decisions. In many cases, AI is not a separate product. It is embedded inside technology customers already use.

That changes the scope of vendor evaluation.

Procurement, security, compliance, and third-party risk management teams need to understand how AI affects data, systems, workflows, and inherited risk. An AI vendor risk assessment must account for those considerations directly rather than assuming they were addressed through a broader review.

Why Traditional Security Proof Points Are No Longer Enough

Traditional security certifications and reports can provide useful information about a vendor’s broader control environment. But unless AI is explicitly included in scope, they may not demonstrate that a deployed AI system has been evaluated against AI-specific security and governance expectations.

A vendor may have a general cybersecurity program, an AI policy, and a broad security report. Those proof points remain valuable. They should not, however, be assumed to answer questions about model dependencies, prompt manipulation, AI system access, sensitive data exposure, or oversight of AI-enabled activity.

For enterprise buyers, the question is becoming more specific:

Has the AI system itself been assessed and validated?

How AI Introduces New Forms of Vendor Risk

When a vendor develops, deploys, or materially relies on AI, its customers may inherit risks involving:

• Model behavior, prompts, outputs, and supporting systems

• Data lineage, sensitive information handling, and potential exposure

• Third-party models, platforms, infrastructure, and other dependencies

• Access controls, permissions, integrations, and authorized tool use

• Monitoring, human oversight, and AI-enabled decision-making

These risks may not be visible in a general assurance report or an AI vendor security assessment questionnaire unless they are specifically defined, assessed, and validated.

Trust Is Now a Procurement Requirement, not a Differentiator

Trust may still help a vendor stand out, but in many enterprise buying processes it is first a gate.

Customers need evidence before they can rely on a vendor whose AI systems access sensitive data, support critical workflows, interact with other systems, or influence decisions. Without that evidence, AI-related questions can delay reviews, create uncertainty, and require additional clarification from security and procurement teams.

Vendors that prepare for those expectations can give buyers a clearer basis for evaluating risk.

The Rise of AI-Specific Due Diligence

As enterprise AI vendor security evaluation practices mature, buyers are asking questions that go beyond general cybersecurity.

They want to know where AI is used, what data it can access, which models and platforms support it, how permissions are controlled, how threats are addressed, and how the organization validates that relevant controls are operating effectively.

An AI vendor risk assessment helps buyers organize those questions. Certification-backed evidence helps vendors answer them with something more reliable than internal claims alone.

What AI Security Certification Actually Signals to Buyers

AI security certification signals that a defined AI system and its supporting environment have been assessed against established requirements.

It does not replace every procurement question or customer review. It provides a stronger starting point by giving relying parties validated information they can examine when making vendor decisions.

Proof of Responsible AI Governance

AI governance defines accountability, acceptable use, review processes, oversight, and responsibilities. Those elements are important, but a policy alone does not prove that an AI system is secure.

HITRUST AI Security Certification helps connect governance expectations to assessable requirements and validated evidence. It shows buyers that defined security and governance practices for the scoped AI system were evaluated rather than simply described.

Evidence of Risk Mitigation and Control Maturity

Buyers also need evidence that appropriate controls exist around the AI system, supporting infrastructure, data flows, integrations, access, and operating practices.

HITRUST AI Security Certification provides a structured way to assess those areas. This gives customers a more defensible basis for understanding how a vendor is addressing AI-specific cybersecurity risk.

For vendors, it provides clearer evidence that the security of the deployed AI system has been evaluated against defined expectations.

How Certification Reduces Friction in the Sales Process

Enterprise buying cycles often require vendors to provide policies, reports, questionnaires, evidence, and follow-up explanations.

Validated AI assurance can help reduce that friction by giving vendors evidence designed to be reviewed and relied upon. The result can be a more focused conversation about scope, risk, and assurance instead of repeatedly rebuilding the vendor’s security story.

Shortening Security Reviews and Questionnaires

An AI vendor risk assessment will still depend on the customer’s needs and the risk presented by the system. Certification does not eliminate due diligence.

It can, however, reduce the need to recreate the same evidence for every buyer. Instead of relying only on questionnaires or other AI vendor security assessment tools, vendors can provide validated assurance that addresses the scoped AI system.

That can help customers focus follow-up questions on remaining risks and business-specific requirements.

Reducing Back-and-Forth with Procurement Teams

Clear assurance evidence can also reduce ambiguity.

When scope, requirements, assessment results, and validation are easier to understand, procurement and risk teams may need fewer clarifications about what was evaluated and how the vendor supports its claims.

For vendors, that can support a more efficient buying process. For customers, it can support more informed and defensible third-party risk decisions.

Where HITRUST Fits into AI Trust and Assurance

HITRUST helps organizations turn cybersecurity assurance into proof that can be used, shared, and trusted.

For vendors building, deploying, or selling AI-enabled systems, that means a path to demonstrate AI-specific cybersecurity assurance. For organizations with TPRM programs, it means a way to request stronger evidence from vendors whose AI systems may introduce meaningful risk.

A Proven Model for Independent, Measurable Assurance

The HITRUST Assurance Program includes assessment, independent validation, centralized quality review, scoring, reporting, and certification.

That process helps make assurance results more consistent and reliable. Rather than relying only on self-reported claims, customers receive validated evidence that can support vendor evaluation and risk decisions.

Extending Established Security Assurance Into AI

HITRUST AI Security Certification extends that assurance approach to deployed AI systems and AI-enabled technologies.

Available as a standalone offering, it provides a focused path for evaluating AI-specific security, governance, and control requirements. This allows vendors to demonstrate that their AI systems have received structured, validated cybersecurity assurance while giving buyers a clearer trust signal during procurement.

Frequently Asked Questions About AI Security Certifications

Why do enterprises require AI security certification from vendors?

Enterprises may require certification when a vendor’s AI system processes sensitive data, supports critical workflows, delivers customer-facing functionality, or materially affects the vendor’s risk profile. Certification gives buyers evidence that the AI system itself was evaluated.

How is AI certification different from traditional security certifications?

Traditional certifications may address a broader organizational or technology environment. AI security certification focuses on the security and governance expectations associated with a scoped, deployed AI system.

Does AI certification impact vendor selection decisions?

It can. During an AI vendor risk assessment, independently validated evidence may give buyers greater confidence and reduce uncertainty. The appropriate level of assurance will depend on the system’s use, scope, data access, and risk.

What are the business benefits of AI security certification?

Certification can help vendors demonstrate trust, respond to customer assurance expectations, reduce repetitive evidence requests, and support more efficient security and procurement reviews.

How can vendors prepare for AI security certification?

Vendors can begin by:

• Defining the AI system, deployment model, and assessment scope

• Documenting data flows, integrations, and system dependencies

• Identifying third-party models, platforms, and infrastructure

• Clarifying ownership of security and governance controls

• Organizing evidence for relevant control practices

These steps can help vendors understand what must be assessed and prepare stronger evidence for customers.

Is AI certification becoming a standard requirement?

AI-specific assurance is becoming a more important consideration, particularly when vendors use AI in systems that affect sensitive data, regulated processes, critical operations, or customer-facing services.

Not every AI use case presents the same risk. Organizations should align assurance requirements to the vendor’s role, the AI system’s scope, and the potential exposure.

Start building trust in your AI systems. Explore HITRUST AI Security Certification today.

Governing Third-Party Information Risk

Organizations depend on third parties to operate and compete. Vendors, cloud providers, software platforms, processors, subcontractors, service organizations, and AI-enabled systems support critical business functions, process sensitive information, and shape operational resilience. This dependence has made third-party information risk a board-level governance issue.

Third-party information risk reaches beyond cybersecurity. A third-party failure can create operational disruption, privacy impact, regulatory exposure, contractual loss, business interruption, reputational harm, customer impact, uninsured financial loss, and continuity failure. The issue for boards and senior management is exposure: what risk the enterprise carries because information, systems, processes, and dependencies sit outside their control.

From Activity Metrics to Exposure-Based Governance

Most organizations have responded by building third-party risk management programs. These programs review vendors, collect assurance reports, request and analyze questionnaires, evaluate contracts, require insurance, manage remediation, and route exceptions for approval. These activities matter. Effective governance also requires a clear view of the exposure those vendors create.

Many organizations still govern third-party risk through activity reporting. Those metrics help track program execution. They rarely show the critical measurements of total residual information risk, total financial exposure, deviations from established norms, peer alignment, concentration risk, retained risk, transferred risk, or required action. A governance model should convert those inputs into a consistent view of residual exposure, confidence, tolerance alignment, concentration, financial impact, retained risk, and transferred risk.

That is the focus of the last installment in HITRUST's The Missing Measure in Information Risk series, Governing Third-Party Information Risk. The paper examines how organizations can move beyond vendor review activity and toward a governance model that measures residual exposure, validates coverage and confidence, compares exposure to defined appetite and tolerance, explains deviations, aggregates risk, benchmarks against peers, and evaluates treatment and transfer.

The paper also reinforces the need for clear accountability between management and the board or risk committee. Management operates the governance model by maintaining the measurement approach, applying thresholds, validating coverage, identifying concentrations, evaluating treatment and transfer, and reporting material exposure. The board or risk committee oversees whether the model is credible and aligned to appetite, while reviewing material exposure, deviations, retained and transferred risk, and confidence in the model.

 

Read Part 1: The Missing Measure in Third-Party Information Risk

Explore why organizations struggle to consistently measure residual third-party risk and why a common risk language is essential for governance, decision-making, and risk transfer. 
 

Read Part 2: The Hidden Weakness in Third-Party Cyber Risk Transfer 

Learn how traditional vendor cyber insurance can create blind spots in third-party risk programs and why risk transfer mechanisms must evolve alongside today's interconnected digital ecosystem.

Read Part 3: Governing Third-Party Information Risk

See what changes when third-party risk reporting shifts from activity metrics to exposure-based governance, giving leadership visibility into the exposure carried, confidence in the view, and action required.