Why CISOs Are Concerned, And Should Be

The concern isn’t about AI itself; it’s about how and where it’s being used. Here are a few reasons why unregulated AI tools have become a top security blind spot:

• Unvetted Data Exposure: Employees may unknowingly input confidential business data into third-party AI systems that store or train on that data.
• Lack of Model Transparency: Many AI tools operate as black boxes. It’s unclear what happens to inputs, how outputs are generated, or how bias and hallucination risks are handled.
• Open-Source Dependencies: AI developers often pull models or libraries from open repositories, introducing supply chain vulnerabilities.
• Policy and Audit Gaps: Most organizations don’t yet have policies to cover AI tool usage, making it hard to assess or enforce compliance.
• Social Engineering and Prompt Injection: Malicious actors are learning to manipulate AI models to leak data or take unintended actions, a new class of attacks that standard security controls don’t yet address.

A Governance Framework for Responsible AI Use

Addressing the shadow AI challenge isn’t about banning tools; it’s about governing them. Forward-looking organizations are building AI governance frameworks that emphasize visibility, accountability, and safe innovation. Key steps include:

1. Discovery and Inventory

• Map all AI tools in use across the organization, whether sanctioned or not.
• Classify by function (e.g., content generation, code review, analytics) and sensitivity of data involved.

2. Policy Definition and Usage Controls

• Define acceptable use policies for AI tools based on risk tier.
• Set guidelines on input restrictions, data retention, and model usage.
• Include AI in your Acceptable Use Policy and Security Awareness Training.

3. Risk Assessment and Vendor Vetting

• Assess AI vendors for data handling, model transparency, security posture, and compliance readiness.
• Prioritize enterprise contracts that include enforceable terms on data usage and model behavior.

4. Monitoring and Enforcement

• Use data loss prevention (DLP) and CASB tools to monitor AI-related activity.
• Create reporting mechanisms for unsanctioned AI use, with education-first enforcement.

5. Education and Culture

• Equip employees with guidance on where and how AI can be used safely.
• Encourage innovation, but make risk awareness part of the cultural norm.

What Security Buyers Should Be Doing

• Launch an AI usage discovery initiative, and know what’s being used before you try to control it
• Build or adapt your security policies to include AI governance
• Classify AI tools based on risk and purpose, not just user role or function
• Vet all AI vendors through the lens of data security, transparency, and compliance readiness
• Treat AI governance as an enterprise-wide initiative, involving legal, compliance, IT, and business leadership

Visibility Before Velocity

In the race to embrace AI, many organizations have skipped a critical step: visibility. But cybersecurity has always taught us that you can’t protect what you can’t see. The same holds true for generative AI. As organizations scale their AI use, the winners will be those who govern first, then accelerate. Because in a world where machines generate content, code, and decisions, the cost of ungoverned speed is risk.