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Publication date: June 25, 2024

What Board Members and Executive Leaders need to know about the Complex AI Governance Landscape

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Written by Angela Polania

Angela Polania, CPA, CISM, CISA, CRISC, HITRUST, CMMC RP. Angela is the Managing Principal at Elevate and board member, and treasurer at the CIO Council of South Florida.

As businesses charge into the Generative AI era, the question of AI governance, in general, is top of mind for everyone – consumers and corporations alike. Businesses want to see how AI can drive their bottom-line goals, while consumers are concerned about privacy. Central to this discussion is the idea that the impact of AI on humanity and the environment is too significant to neglect regulation and governance.

To reap the benefits of AI, businesses have to integrate it in a transparent, responsible, safe, and trustworthy manner. This can only be achieved if they operationalize AI governance within their data and AI ecosystem. Gartner refers to AI governance as AI TRiSM (AI Trust, Risk, and Security Management), whose purpose is to ensure AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection.

The potential benefits of establishing proper AI governance are attractive: Gartner predicts that, by 2026, businesses that implement AI transparency, trust, and security will see a 50% rise in AI adoption and achievement of business goals.

Why AI Governance is Non-Negotiable

The risks associated with unregulated AI can be catastrophic. From financial institutions to major brands to government agencies, there are numerous cautionary tales about recent AI-related harms. The AI Incident Database contains around 3,000 examples of such disasters, which have resulted in significant costs for organizations worldwide.

It is important for enterprises to have the right controls and oversight in place to avoid risks associated with blind spots, shadow AI, data opacity, unsecured models, uncontrolled interactions, compliance violations, and other vulnerabilities that can have negative consequences for a company’s finances and reputation. Research by ISACA indicates that 60% of respondents are concerned about the potential for malicious use of generative AI.

Many questions and uncertainties still surround the use of generative AI tools and technologies leaving privacy and security teams navigating uncharted waters. The biggest business risk is that consumers will lose trust, making responsible AI a “commercial imperative.” A report by Thales indicates that 89% of individuals expect transparency, accountability, and fairness from organizations, making AI governance a non-negotiable strategy.

Key Pillars of Effective AI Governance

AI can permeate every corner of business and life, so its mitigation cannot be left solely to the CISO. Reining in AI, particularly generative AI, requires cross-collaborative effort within each organization. The following roles are essential to effective AI governance:

Key Pillars of Effective AI Governance

Creating an “AI A-Team” of sorts is the first step to managing AI governance in your organization. However, maintaining governance initiatives as fresh, relevant, and agile as AI itself will be a continuous challenge given the speed of AI advancement.

Strategies for Implementing AI Governance

If ungoverned AI is a recipe for disaster. Trustworthy and responsible AI is the path to improved business value through safe and reliable innovation. Responsible AI begins with taking control of your AI landscape. Here’s how:

  1. Discover and catalog your AI use cases across all your technology stack, on-premises and in the cloud.
  2. Understand data sources used, produced, or managed with AI use cases.
  3. Inventory and evaluate models against regulatory standards and risks such as bias, efficiency, explainability, and accountability.
  4. Enable continuous data mapping to understand the data sources feeding your AI systems and connect your AI systems to data sources, processes, third-party vendors, and potential risks.
  5. Align data and AI controls to ensure compliance with governance, security, and privacy standards.
  6. Automate management of compliance with a complex web of regulatory standards and laws, such as NIST AI RMF and EU AI Act.

Enterprises that successfully carry out these five steps and implement sound AI governance practices will spearhead responsible innovation that translates to business value.

Conclusion

By managing AI risk and getting ahead of the unwanted consequences that can come from uncontrolled or unregulated AI models, companies can get on the path toward accelerated business value and unlock unprecedented benefits. With the right practices in place, savvy companies can strategize AI governance to ensure the safe, trustworthy, and compliant use of AI across your business.

At Elevate, we specialize in providing tailored AI governance services. Contact us to schedule a consultancy meeting.

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