Artificial intelligence governance is at a crossroads. Companies across industries are integrating AI into their products and operations at an unprecedented pace, but governance frameworks are struggling to keep up.
Many organizations still treat AI governance as a compliance checkbox, documenting risks at a single point in time and relying on static policies. However, AI is not static — it is dynamic, evolving and often entangled with third-party systems that change unpredictably.
The challenge for legal and compliance teams extends beyond merely following new AI regulations. Organizations must build governance structures that adapt in real-time to ensure AI systems remain compliant, explainable and accountable long after deployment.
Moving from a compliance-driven approach to an adaptive, living governance model will help organizations mitigate risk, avoid regulatory blind spots and build trust in AI systems.
Static compliance vs. living governance
Traditional AI governance models rely on pre-deployment risk assessments, contractual safeguards and compliance documentation. While these are essential, they are insufficient in managing AI's continuous evolution.
For example, companies using large language models from external vendors may experience unexpected biases due to silent updates, unmonitored data flows or changes in training policies. Additionally, model drift — the gradual erosion of AI performance as real-world data diverges from the model's original training distribution — poses significant risks in high-stakes applications such as automated hiring, credit underwriting and health care diagnostics.
To mitigate these risks, AI governance must be proactive, integrated into product life cycles, and continuously evolving. Organizations should move beyond one-time compliance checks and adopt governance structures that emphasize real-time risk monitoring, iterative oversight and cross-functional collaboration.
Continuous risk monitoring and auditing
AI systems should be monitored throughout their life cycle, not just during initial deployment. This requires integrating real-time model auditing, bias detection and compliance drift tracking to flag anomalies before they lead to regulatory violations or reputational damage.
While many companies conduct Service Organization Control 2 audits, AI systems require specialized governance mechanisms.
AI-specific audits should include algorithmic impact assessments, bias audits and fairness testing, and explainability and interpretability evaluations.
The EU AI Act introduces stricter requirements for high-risk AI applications, including mandatory transparency measures and accountability structures. Similarly, the U.S. National Institute of Standards and Technology's AI Risk Management Framework provides a structured approach to identifying, measuring and mitigating AI-related risks.
The Business Software Alliance emphasizes the importance of AI risk frameworks, calling for standardized governance models that can adapt to evolving regulations.
Vendor and third-party AI oversight
Reliance on third-party AI models introduces an additional layer of complexity. Many organizations license AI models from external vendors but have limited insight into how these models are trained, updated or governed. This lack of transparency increases compliance risks, particularly when vendors introduce unannounced updates that alter AI outputs.
To address this, organizations must include contractual AI governance clauses requiring model update disclosures and training data transparency, and conduct vendor risk assessments that go beyond traditional cybersecurity audits to assess AI-specific risks.
A major compliance challenge is data provenance — understanding where training data originates and how it is used. If a vendor repurposes customer data for model fine-tuning, the EU General Data Protection Regulation, California Consumer Privacy Act, or sector-specific data protection laws may be triggered.
To prevent compliance violations, vendor agreements should explicitly restrict unauthorized data usage and provide audit mechanisms for ongoing oversight. Research from AI & Society highlights that as AI adoption grows, governments and enterprises must establish robust third-party risk governance frameworks to prevent AI failures and hold vendors accountable for transparency and compliance. The study also suggests AI procurement policies should mandate vendor accountability by requiring ongoing audits and public reporting of AI updates and risk assessments.
Governance as a cross-functional responsibility
AI governance is not just a legal issue — it must be embedded within product design, engineering and corporate strategy. Organizations should establish cross-functional AI governance committees that ensure collaboration between: legal teams — compliance and risk assessment; product developers — explainability-by-design; and privacy and ethics teams — bias mitigation and fairness.
A major governance challenge is balancing AI explainability with intellectual property protection. While regulators demand transparency, many AI models operate as black boxes, making it difficult for compliance teams to justify AI-driven decisions.
To resolve this, organizations should implement explainability-by-design strategies, adopt AI fairness frameworks — such as those recommended by the NIST AI Risk Management Framework — and leverage AI ethics boards to review high-risk AI applications.
Additionally, AI governance should enforce privacy-enhancing techniques to comply with the GDPR and CCPA. AI-driven personalization engines must ensure they minimize unnecessary data collection, obtain explicit user consent and prevent unintended profiling.
In "Principles of AI Governance and Model Risk Management," James Sayles argues establishing cross-functional AI governance teams is critical to ensuring AI is fair, unbiased and explainable. Sayles' research highlights that many organizations struggle with fragmented governance and uncoordinated AI oversight, reinforcing the need for integrated, cross-functional governance frameworks for AI risk management.
Preparing for the future of AI regulation
The EU AI Act, U.S. state laws and sector-specific AI regulations are pushing companies toward structured AI governance. Organizations that wait for regulations to dictate compliance will struggle to adapt. Proactively investing in adaptive AI governance now will help companies remain compliant without major operational disruptions.
A critical, yet often overlooked governance issue is AI liability. If an AI system generates harmful or misleading outputs, who is responsible — the developer, vendor or end user?
To mitigate liability risks, organizations should define clear accountability structures within AI policies, consider AI liability insurance and include indemnification clauses in vendor agreements.
AI governance as a competitive advantage
Organizations that adopt adaptive AI governance will not only mitigate risks but gain a competitive advantage. The next phase of AI governance will not be defined by rigid policies but by dynamic, proactive oversight.
Companies that embed real-time monitoring, vendor accountability and cross-functional governance structures will lead in responsible AI innovation — setting the new industry standard.
Anjella Shirkhanloo, CIPM, is senior counsel, privacy at Alteryx.
