Businesses are quickly learning about the transformational potential of AI as adoption accelerates. But just as rapidly, business leaders are confronting a series of regulation, security, and ethical AI use challenges.
Artificial intelligence is growing by leaps and bounds and plays an ever-greater role in decision-making across industries, from finance and healthcare to legal and HR functions. And leaders in those industries are seeing the importance of airtight AI governance.
Without this governance, organizations risk falling out of compliance with regulations, having their security breached, being made to pay fines, and suffering reputational damage.
Few businesses can afford outcomes like these.
That is why organizations must implement structured and thoughtful AI governance strategies that ensure their AI models are secure, explainable, and aligned with legal and ethical requirements.
Key Steps
Fundamental to effective AI governance is a risk-based approach. The approach involves establishing a comprehensive AI model inventory that maps out all AI-driven processes within an organization. This inventory should include any third-party applications that use AI, with descriptions of their use.
Once the inventory has been created, businesses should move on to assigning risk rankings to their AI models based on their business impact, the regulatory requirements that govern them, and any possible vulnerabilities in security.
Frameworks like NIST AI RMF offer a structured approach for charting progress with governance programs and identifying gaps as programs evolve. High-risk AI applications—for example, those that involve financial transactions, healthcare diagnostics, or legal compliance—should be prioritized for stricter oversight and enhanced security measures.
For organizations, these steps begin a governance framework that increases transparency and accountability while mitigating risk.
AI Security Must be Top-of-Mind
Last year, many organizations underestimated AI security risks. This year will be different.
AI has helped cyber attackers refine their strategies, and at the same time, AI models have become the newest target for attacks. With more opportunities for innovation come new risks.
This means security is paramount as organizations continue to roll out this game-changing new technology.
The first step in securing AI systems is creating visibility, which is fundamental in creating an inventory of AI assets. By identifying potential weaknesses early, businesses can allocate security budgets more effectively and ensure critical AI applications are protected against exploitation.
Data breaches, compliance violations, and operational disruptions are simply not an option for most organizations.
Penetration testing and vulnerability assessments have gone from being nice-to-haves to must-haves regarding AI security. By testing AI systems before something goes wrong, organizations can:
- Detect and mitigate any security flaws before a bad actor notices them.
- Ensure AI models function as they are meant to, which can reduce bias and prevent unintended consequences.
- Guide budget planning and steer the proper resources to AI security.
Focusing on business-critical AI models first ensures maximum protection where it matters most.
Scaling AI Governance
AI governance is not static but something that must evolve alongside the growth of an organization, changes to the law, and new types of threats.
Organizations must do more than simply think about continuous monitoring, advanced employee training, and governance enhancements to maintain effective governance. These are initiatives they must invest in.
AI models should be regularly evaluated for performance drift (or “Model Drift”), compliance risks, and security vulnerabilities. Teams responsible for AI oversight—including IT, compliance, and risk management—must also be regularly evaluated to ensure they are equipped with up-to-date knowledge through ongoing training and industry engagement. By integrating AI governance into enterprise-wide risk management initiatives, businesses can stay ahead of regulations and security threats –which never stop evolving.
To make AI governance robust and effective, organizations must think about ongoing proactive steps.
Organizations must eliminate the barriers between IT, risk management, and various other business units to prepare for and mitigate AI risk. They should establish AI working groups that bring together diverse expertise to address complex AI risks. Regular meetings and clear communication channels are essential for effective collaboration.
Technology can be a huge enabler here to facilitate this collaboration and ensure everyone is working from the same data.
AI governance must be treated as an active strategic priority rather than a reactive compliance exercise. Businesses investing in robust AI governance will be better equipped to embrace the opportunity AI offers while navigating the complexities of regulatory changes, threats from cyber attackers, and competitive pressures this year and far into the future.
