The Overlooked Enterprise Threat of 2025 and What You Must Do in 2026
In 2025, one of the most overlooked critical risks in enterprise cybersecurity was AI risk hidden within third-party vendor ecosystems, especially as organizations rapidly adopted generative AI, embedded AI features, and AI-assisted workflows across their technology stacks.
What was often treated as a “product feature” or a convenience tool quietly became a ‘significant blind spot in third-party risk management’.
As we move into 2026, risk owners, CISOs, and compliance leaders must finally confront this vulnerability head-on if they intend to govern AI responsibly and protect sensitive data, operational integrity, and regulatory compliance.
Did You Know There Are Hidden AI Risk Lurking Inside Your Vendor Stack?
By 2025, AI had quietly embedded itself into the enterprise technology stack, often outside the visibility of IT and security teams. According to industry research,
‘89% of enterprise AI usage was invisible to organizations’,
with most AI interactions happening without central oversight. At the same time, unauthorized AI tools (Shadow AI) have proliferated, with a surge in usage and data flows that bypass corporate governance. Skyhigh Security WithSecure™
This lack of oversight has real risk implications. A recent industry report found that in 2025,
‘20% of organizations experienced breaches linked to unauthorized AI activity’,
with an average breach cost increase of approximately $670,000 due to Shadow AI exposures. Meanwhile, sensitive data misuse is widespread: organizations are now encountering an average of 223 data policy violations per month involving generative AI, many involving regulated personal or financial information. IT Pro Ignition Technology
Despite this rapid adoption and associated risk,
less than 10% of enterprises have implemented data
protection controls specifically for AI tools,
leaving the vast majority of AI usage exposed to unmanaged threats. This gap between adoption and governance means that AI is no longer just a convenience or competitive advantage; it has become a strategic risk that sits largely outside traditional third-party risk and security programs. Skyhigh Security
As organizations entered 2026, AI risk within the existing vendor ecosystem emerged not as a future concern but as a present-day threat demanding urgent attention. With AI capabilities embedded in business-critical applications and third-party tools, there is still a critical need to expand visibility, governance, and risk controls into AI-centric vendor risk assessments if enterprises expect to secure data, ensure compliance, and maintain operational resilience.
The AI Adoption Curve
Has Outpaced Enterprise Visibility
- AI Is Everywhere: AI adoption soared in 2025. Enterprises ramped up usage of generative AI more than sixfold in under a year, creating a massive attack surface powered by AI systems that many IT programs weren’t prepared to secure.
- Third-Party AI Is the Norm: Surveys suggest that as many as 78% of organizations use third-party AI tools, with more than half relying exclusively on them. Yet more than half of all AI failures originate from third-party tools, meaning that hidden vendor AI risk is now systemic, not hypothetical. MIT Sloan
- Visibility Gaps Are Huge: According to industry reports, 64% of organizations lack full visibility into their AI risk exposure, and nearly half have no AI-specific security controls. Without visibility into what third-party tools are actually doing with data, organizations cannot make informed risk decisions, and they can’t govern what they don’t see. Help Net Security
- Sensitive Data Is Exposed Frequently: Recent research shows that the average enterprise experiences hundreds of data policy violations per month involving AI tools, with many incidents involving regulated personal, financial, or healthcare data. IT Pro
In short, AI is being adopted faster than it is being secured, and third-party vendor AI is a critical blind spot in current governance programs.
What Are the Risk Gaps
In AI Tools Used by Vendors?
Third-party AI risk sits at the intersection of traditional vendor risk and emerging AI vulnerabilities, and it exposes gaps that many risk programs were never designed to handle:
- Black-Box and Transparency Gaps: Many AI models in vendor stacks are opaque. Organizations often have no idea how these models were trained, what data they process, or how they make decisions. This opacity hinders due diligence, auditing, and compliance. Akitra
- Hidden Data Exposure: Third-party AI tools may ingest your data sometimes permanently, and organizations rarely get contractual assurances about how that data is stored, used, or whether it can be used to train other models. When vendors don’t disclose training practices or data handling policies, enterprises assume reputational and regulatory liability by default. BigID
- Compliance Misalignment: AI vendors often aren’t aligned with key regulatory requirements like the EU AI Act, ISO/IEC 42001, or sector-specific privacy laws. When these tools handle regulated data or operate across jurisdictions, enterprises can find themselves in violation simply because the vendor’s AI isn’t compliant. Akitra
- Cybersecurity Gaps: Third-party AI systems introduce novel cybersecurity exposures, from prompt injection and model inversion attacks to insecure APIs and data leakage paths that weren’t anticipated by traditional security controls. Browse AI Tools
- Contractual & Liability Gaps: Contracts often do not include robust AI risk language. Vendors may refuse to share critical details about model behavior, training data, or security testing, leaving organizations without enforceable protections. Forbes
AI-Layer Risks
Unique Threat Vectors in Vendor AI
AI introduces a “risk layer” that traditional vendor risk management (TPRM) does not cover. These include:
- Model Bias and Ethical Risks: Third-party models trained on skewed or unvetted data can produce biased, discriminatory, or legally problematic outcomes, triggering ethical and compliance concerns. VerifyWise
- Operational Cascade Risk: A malfunction or misconfiguration in a vendor’s AI feature. For example, a logistics platform or customer system can propagate downstream and disrupt business processes beyond the immediate vendor relationship. HALOCK
- Vendor Ecosystem Concentration Risk: Many vendors rely on the same foundational AI services (e.g., major LLM providers). If that common dependency fails or is compromised, it can lead to correlated failures across multiple vendor platforms, thereby compounding enterprise risk. Atlas Systems
- Insider and Shadow AI: “Shadow AI”, the unsanctioned use of personal AI tools by employees, remains a serious blind spot, increasing the risk of uncontrolled data flows and unauthorized AI usage. TechRadar
- Lack of Continuous Assurance: Unlike static systems, AI models change frequently. New versions, updated training data, and evolving APIs can rapidly shift risk profiles. Without ongoing monitoring and built-in governance, even previously vetted systems can become threat vectors.
5 Things to Keep in Mind
When AI Enters Your Vendor Stack
To effectively govern third-party AI risk, organizations must adapt their risk practices:
- Inventory and Identify AI Dependencies: Create and maintain an up-to-date inventory of all AI usage across internal and external systems, including embedded AI features in vendor tools. Mitratech
- Demand AI Transparency and Contractual Rights: Require clear vendor disclosure on:
- Model usage
- Data access policies
- Training data sources
- Security and audit rights Forbes
- Integrate AI Into Third-Party Risk Assessments: Adjust your TPRM processes to include AI-specific checks, from ethical impact to security testing, rather than treating AI as a secondary concern. ai
- Implement Continuous Monitoring: Static risk assessments are no longer sufficient. Adopt tools and processes to continuously monitor AI risk, flagging changes in data flows, model behavior, or usage anomalies. Optiv
- Cross-Functional Governance: AI risk is not just a security concern; it requires alignment across legal, procurement, risk, and business units to ensure all stakeholders understand exposure, controls, and accountability. Forbes
How Accorian Helps Organizations Address Third-Party AI Risk
As AI becomes deeply embedded across enterprise vendor ecosystems, organizations can no longer rely on traditional third-party risk models to manage exposure. Addressing AI risk requires structured visibility, defensible governance, and continuous assurance, not reactive controls.
Accorian helps organizations operationalize third-party AI risk management by embedding AI-specific controls into existing security, privacy, and GRC programs. Through comprehensive vendor AI risk assessments, Accorian enables enterprises to identify where AI is being used across their vendor stack, how data is flowing, and where hidden compliance and security gaps exist. These assessments are aligned with emerging standards and regulatory expectations, including ISO/IEC 42001, NIST AI RMF, and global privacy frameworks.
Beyond assessments, Accorian supports organizations in institutionalizing AI governance, from updating third-party due diligence and contractual requirements to integrating AI risk into ongoing monitoring and audit workflows. This ensures that AI risk management is not a one-time exercise, but a sustained, scalable capability as vendor environments evolve.
By combining deep cybersecurity expertise with practical governance frameworks, Accorian enables organizations to move beyond awareness and into execution, helping them manage AI risk with clarity, confidence, and accountability as they navigate 2026 and beyond.
