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Navigating AI Security and Assurance: AI Security Requires More than Governance
Artificial intelligence is rapidly moving from experimentation into business-critical operations. The next stage of adoption will increasingly depend on agentic AI systems that can access enterprise data, interact with other systems, and take actions with varying degrees of human oversight.
This transition can create significant business value, but it also expands the potential consequences of insecure AI across organizations and their extended vendor ecosystems.
Traditional information security and AI governance remain essential. Organizations also need reliable evidence that deployed AI systems are protected against AI-specific threats such as prompt injection, data poisoning, and the misuse of privileges granted to AI agents and applications.
Agentic AI Changes the Risk Equation
Organizations continue to expand their use of AI. According to McKinsey’s The State of AI: Global Survey 2025, 88% of organizations report using AI in at least one business function, while 62% of organizations using AI remain in the Experimenting or Piloting phase. Deloitte’s 2026 State of AI in the Enterprise report found that 74% of companies plan to deploy agentic AI within the next two years.
However, relatively few companies have the AI security programs necessary to align with their AI adoption. F5’s 2025 State of AI Application Strategy Report found that 96% of organizations are implementing AI models, but only 2% indicate they are “highly ready” for the challenges of their AI deployments.
The transition from generative AI tools to agentic AI systems represents a meaningful change in organizational risk. Generative AI systems primarily produce content or recommendations. Agentic AI systems may also access data, call APIs, use software tools, communicate with other agents, and take actions on behalf of users or organizations.
As organizations grant AI systems more authority, they can also increase the consequences of a security failure. Many organizations will adopt agentic capabilities through third-party applications and vendor-managed AI services. As a result, an organization’s AI security will increasingly depend on controls that companies throughout its technology and data supply chain implement.
Comprehensive AI Assurance Requires Four Domains
Comprehensive AI assurance requires visibility across four complementary domains:
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Traditional information security governance
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Traditional information security
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AI governance
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AI security
Together, these domains help organizations determine whether they appropriately govern an AI-enabled system, whether they secure its underlying IT environment, and whether they implement protections for threats that arise specifically from the use of AI.
Many organizations use NIST AI RMF to define AI governance controls and ISO 42001 to demonstrate AI compliance to stakeholders. ISO 42001 provides valuable guidance for establishing AI management systems, accountability structures, and risk management processes. However, this level of AI compliance does not demonstrate that organizations have addressed system-level AI threats within their environments.
ISO 42001 and HITRUST AI Security address different layers of assurance. ISO 42001 focuses primarily on governance, accountability, risk management, and continuous improvement. HITRUST AI Security focuses on security controls for deployed AI systems and their operational protection.
In simple terms, ISO 42001 answers the question, “Are you governing AI responsibly?” HITRUST AI Security Certification answers the question, “Is your AI system secure?”
AI Access Controls Become Critical as Agents Gain Authority
Agentic AI increases the importance of identity, access management, and least privilege because an agent may access enterprise data, interact with applications, invoke APIs, or take actions on behalf of a user. If an agent processes malicious instructions or operates with excessive permissions, an attacker may exploit that authority to access information or perform actions the attacker could not execute directly.
According to IBM’s Cost of a Data Breach Report 2026, 92% of organizations that experienced an AI-related breach lacked proper AI access controls.
Organizations therefore need to evaluate not only whether access controls exist, but also whether they appropriately restrict and monitor the permissions, tools, data, and actions available to an AI system.
Building Assurance for Deployed AI
As AI becomes more embedded in critical business processes, organizations will need more than evidence that they govern AI. They will need reliable evidence that they protect systems using AI against threats associated with their data, models, and actions.
For third-party risk management programs, that means identifying vendors with AI-enabled systems that can access sensitive information, support important operations, make consequential decisions, or take actions within the organization’s environment. TPRM programs should establish risk-based assurance expectations that address both foundational cybersecurity controls and AI-specific threats.
For organizations deploying AI, that means identifying the AI-enabled systems that customers and partners rely upon and demonstrating the security of those systems through a consistent, independently validated approach.
Read Navigating AI Security & Assurance to explore the complete AI assurance landscape and considerations for organizations deploying AI and managing third-party risk.
Navigating AI Security and Assurance: AI Security Requires More than Governance Navigating AI Security and Assurance: AI Security Requires More than Governance
Introduction
Closing the assurance gap requires a new operating model that treats third-party risk as a continuous, ecosystem-wide discipline rather than a periodic, snapshot in time compliance exercise that’s cyber-threat adaptive. That model must align people, process, tools, and reliable vendor cyber assurance.
The need for continuous ecosystem trust
Continuous ecosystem trust treats third-party risk as a dynamic discipline of monitoring and validation of new vendors and emerging threats across the existing portfolio. It is a coordinated operating model across the people responsible for assessing vendor risk, the broader vendor management team (Procurement, Legal, Risk), the processes they follow, the tools they use, and the assurance evidence that supports decision-making.
Governance must come before technology. The operating model needs named owners, decision rights, risk tolerances, escalation paths, and alignment across teams. Otherwise, faster tools are likely to automate an unclear process rather than improve decision-making.
Organizations need people who can interpret threat intelligence, evaluate vendor evidence, connect findings to business impact, and communicate defensible decisions to leaders. The security function also needs a defined role in procurement and vendor management so risk is considered before onboarding and throughout the partner relationship.
These are the characteristics I most commonly observe in modern TPRM programs when talking to CISOs and Heads of TPRM programs.
- Classify vendors according to (perceived) inherent risk, including data sensitivity, level of access, operational importance, replaceability, and concentration risk.
- Built on control standards that are relevant based on up-to-date threat intelligence that ensures vendors are assessed against the types of attacks that are happening now, not the ones that happened last year or longer.
- Apply assessment depth according to exposure, defines monitoring and escalation, and requires remediation when evidence changes.
- Use of risk tiering and validated assurance should determine where deeper review is necessary and where existing evidence is sufficient, allowing limited resources to focus on relationships that create the greatest business exposure.
- Convert evidence into a consistent view of residual exposure that drives decision-making. That requires standardization of controls evaluation, comparable evidence, assurance weighting, decision thresholds, and portfolio reporting.
- The ability to scale and be more efficient as the number of vendors and suppliers adopted by an organization grows.
- Activity metrics still matter, but leadership must understand what exposure remains and what investments and action should follow.
A scalable, relevant, and reliable approach to controls assurance
HITRUST is designed to replace fragmented, self-attested evidence with independent, benchmarked, and quality-controlled assurance. Its approach applies threat-adaptive control requirements, independent assessment, centralized quality review, and consistent scoring. Those characteristics can make evidence more comparable and defensible across a broad vendor population.
The 2026 HITRUST Trust Report supports that 99.62 percent of HITRUST-certified environments did not report a security breach in 2025 and more than 80 percent of HITRUST certifications, including 100 percent of r2 certifications, address threats posed by service providers.
HITRUST also supports a tiered approach so assessment effort can match vendor exposure. Our “assess-once, report-to-many model” allows validated results to be reused across customers, reducing redundant reviews.
AI-specific assurance must be in scope
As mentioned in the previous blog, AI changes both the threat environment and what must be assessed. Traditional cybersecurity reports can remain valuable over time, but they should not be assumed to cover AI threat vectors unless those areas are explicitly in scope (and supported).
HITRUST AI Security Certification is designed for deployed AI systems and AI platforms. It combines defined AI-specific security and governance requirements with assessment, independent validation, centralized quality review, scoring, reporting, and certification. For vendors, this can turn trust from a claim into evidence. For buyers, it can provide a stronger starting point for due diligence while preserving business-specific review of scope, use, data access, and residual risk.
Threat relevance must also be maintained. The HITRUST Cyber Threat Adaptive program uses threat intelligence, vulnerability research, and real-world attack data to keep assurance requirements aligned with adversary behavior. HITRUST threat analysis also includes MITRE ATLAS for adversarial techniques targeting AI systems.
Continuous ecosystem trust changes the questions leaders can answer
A modern TPRM program should enable security and risk leaders to answer five practical questions:
- Is this vendor continuously trustworthy? Combine ongoing monitoring with refreshed, validated evidence so trust reflects current conditions.
- Can I make a defensible decision based on current evidence? Use independently validated and consistently scored results that support discussions with executives, boards, regulators, customers, and third parties.
- How do I compare hundreds or thousands of vendors consistently? Apply standardized control requirements and scoring across the vendor population for normalized information-risk reporting, with assessment depth matched to risk.
- How do I quantify residual risk across my supply chain? Connect comparable assessment results with business context, exposure, remediation status, risk appetite, and concentration to understand the risk that remains.
- How do I reduce assessment fatigue while improving assurance? Focus additional reviews on areas where exposure or current signals justify it.
At the portfolio level, leaders should aggregate exposure, compare it with defined appetite and tolerance, identify concentrations, and distinguish retained risk from risk transferred through contracts or insurance. Contracts and insurance may shift financial exposure, but they do not eliminate operational or information risk.
The takeaway
Modern TPRM programs require more than periodic assessments. They depend on a coordinated approach that combines skilled people, disciplined processes and practices, standardized and validated assurance, and continuous monitoring to deliver continuous ecosystem trust. The investment in this approach moves TPRM from collecting compliance artifacts to actively managing ecosystem-wide risk and trust. By leveraging a threat-relevant, reliable, and scalable assurance model with HITRUST, organizations can make more streamlined, consistent, and robust decisions across their vendor ecosystems, building trust across all parties.
From Checklists to Continuous Ecosystem Trust: The Future of TPRM From Checklists to Continuous Ecosystem Trust: The Future of TPRM
Third-party risk management (TPRM) teams rely on assurance reports to help evaluate vendors, suppliers, and business partners. But having an assurance report is not the same as having the information needed to make a risk decision.
That distinction was at the center of The Assurance Gap: What 103 SOC 2 Reports Actually Showed Us, a recent HITRUST webinar featuring AJ Yawn, Author and GRC Engineering Lead at Rippling, and HITRUST's Ryan Patrick. The conversation explored findings from an analysis of more than 100 SOC 2 reports and asked a fundamental question: Are the assurance reports organizations rely on actually providing visibility into the risks that matter most?
That analysis is now detailed in The Assurance Gap, a new HITRUST paper.
The purpose of the analysis is not to suggest that SOC 2 has no value. A SOC 2 Type 2 report can provide detailed information about a service organization, its controls, testing, and identified exceptions. But the flexibility inherent in SOC 2 also means that scope, control selection, testing, and other elements can vary from report to report.
For organizations relying on those reports to make third-party risk decisions, understanding what the report actually provides is critical.
Ransomware recovery requires more than a report label
One of the findings Ryan highlighted during the webinar was particularly stark: 0% of the sampled SOC 2 reports included an organizational control requiring offline or immutable backups.
Offline or immutable backups can play an important role in recovering from ransomware by helping organizations restore data without relying on backups that may also have been compromised.
The issue for a report recipient is visibility. The absence of an offline or immutable backup control from a SOC 2 report does not mean the organization does not have those backups. It means the report does not provide evidence that allows the recipient to confirm the control.
That distinction becomes especially important when an organization is using the report to evaluate a third party. If ransomware affects a critical vendor, the TPRM team needs to understand whether that vendor can respond and recover. If the assurance artifact does not address a control that is material to that decision, additional evidence may be necessary.
During the webinar, Ryan and AJ also connected several of the findings as parts of the same potential attack chain. Phishing may provide an initial entry point. Multi-factor authentication can help prevent compromised credentials from being used. If ransomware is ultimately deployed, recovery capabilities such as offline or immutable backups become critical.
Yet the analysis found gaps in the visibility provided by the sampled reports across each of those areas.
Phishing remains persistent, but assurance coverage was limited
Of the 103 SOC 2 reports analyzed, only 7% included a control for dedicated phishing training or simulations, while only 5% included a prescriptive control requiring an email filtering solution to block suspicious emails and unnecessary file types before they reached employee inboxes.
Only 1% included controls addressing both requirements.
This does not mean the organizations represented in the sample were not conducting general security awareness training. In fact, the analysis found that most included general security awareness training. What was far less common was explicit coverage of dedicated phishing training, simulations, and preventive email filtering controls.
AJ focused on that distinction during the webinar. Phishing is not a new or obscure cyber risk. It is a well-understood threat, which makes the limited visibility in the sampled reports especially important for organizations relying on those reports to make third-party risk decisions.
As AJ put it during the discussion, if organizations are trusting third-party reports to make decisions, those reports should at a minimum be relevant to the existing threat landscape.
That is the broader issue behind the findings. Cyber threats continue to evolve. Assurance must provide enough relevant evidence for recipients to understand whether controls addressing those threats are actually included and validated.
What does the report actually allow you to conclude?
The ransomware and phishing findings point to a larger question: What risk decision is the assurance report actually supporting?
Organizations often need assurance artifacts because evaluating every control at every vendor independently is not scalable. A reusable assurance report can reduce significant duplication for both vendors and TPRM teams.
But reuse only works when the artifact is fit for the decision.
The burden is placed on the person consuming the report. That person must determine what was tested, whether it is relevant, and whether the report provides the information needed to make a risk decision. For teams responsible for hundreds or thousands of third parties, performing that level of interpretation across every report can quickly become difficult to scale.
This is where the assurance gap emerges. A familiar report label can create confidence, but the underlying evidence may not necessarily address the service, threat, dependency, or risk decision that matters to the recipient.
The Assurance Gap paper outlines five questions organizations can use when evaluating any assurance artifact:
- Does the scope cover the provided service?
- Is the assurance responsive to the expected risks and threats?
- Who prepared and quality reviewed the work?
- What service providers are included, and are they assessed?
- Can two reports be measurably compared?
These questions shift the focus from whether a vendor simply has an assurance report to whether the evidence in that report is sufficient for the decision being made.
Move from assurance labels to evidence that supports the decision
SOC 2, HITRUST, ISO 27001, PCI DSS, NIST CSF, questionnaires, and other assurance mechanisms were designed for different purposes. Treating them as interchangeable can create gaps between what an organization believes has been validated and what the underlying evidence actually supports.
The goal is not to collect more assurance artifacts. It is to understand what each artifact proves, what it leaves open, and whether additional evidence is necessary.
The Assurance Gap expands on the findings discussed by AJ and Ryan, including threat-landscape coverage, quality, supply chain coverage, measurable outcomes, and the questions TPRM teams can use to evaluate the evidence they rely on.
The conversation continues
Register to attend SOC 2 vs. HITRUST: A Practitioner Debate on What Third-Party Risk Actually Needs on October 8 at 10:00 AM CT to hear Nick Norton, AJ Yawn, and Ryan Patrick explore what SOC 2 and HITRUST each provide for third-party risk management, where the gaps remain, and what TPRM teams need from assurance today.