Shadow AI in the enterprise: Managing risk without slowing progress

Partner content As AI innovation accelerates, many global leaders are advocating for limited regulations to keep development unimpeded. This open approach has given organizations room to experiment with AI tools, models, and capabilities that promise big gains in efficiency and insight. But with that flexibility comes a growing threat landscape that IT must be ready to tackle.

Cybercriminals are also taking advantage of AI tools to execute more sophisticated attacks. Delinea's recent Cybersecurity and the AI Threat Landscape report found a marked increase in AI-driven threats in 2024. Phishing campaigns generated by AI, deepfake-based fraud, and malware that harvests credentials are bypassing traditional defenses. Weak data protection hygiene and poor credential rotation made breaches like the Snowflake incident worse, proving that even strong AI can't fix weak identity security practices.

On top of all this, enterprise IT leaders are also grappling with another challenge: shadow AI.

When AI goes rogue: What IT leaders must know about shadow AI

Shadow AI is the term for employees or departments using AI tools without first consulting IT or security teams. It has become an urgent issue as the technology becomes more commercial. Easy access to AI tools online enables users to bypass procurement protocols, often skipping vetting and compliance checks altogether.

These 'off-the-grid' deployments introduce unmanaged risk. Sensitive data may be exposed, regulatory obligations ignored, and existing defenses circumvented. In sectors such as healthcare, government, or financial services, this kind of oversight can carry serious legal and operational consequences.

Tackling shadow AI requires a tech-led approach. IT and cybersecurity leaders must take responsibility for setting guardrails that support innovation while reducing friction and exposure. Here's how:

Strong governance starts with clear guidance - not just written policy, but practical processes and decision frameworks. Define what acceptable AI use looks like and create a cross-functional working group that includes legal, security, and business stakeholders to review new tools quickly. Fast-paced innovation requires responsive oversight, not bureaucratic delay.

You can't secure what you can't see. Implement tools that automatically scan for both approved and unapproved AI services operating inside your environment. Create an inventory and compare it against existing policies. This gives you a starting point for enforcement and education.

Once AI usage is mapped, apply access controls aligned with zero trust principles. Restrict AI tools to the minimum data and systems needed. Integrate these tools with existing identity systems, enforce least privilege by default, and rotate credentials frequently to avoid theft.

Even compliant tools can be misused. Behavioral monitoring powered by automation helps you spot red flags early. Look for unexpected patterns like spikes in outbound traffic or AI scripts accessing sensitive files. Quick alerts let IT teams act before small issues become major breaches.

The best defense is an informed workforce. Roll out training that goes beyond basic AI literacy. Help teams understand which tools are safe, what the risks look like, and how to raise concerns when they spot something off. Make secure AI adoption part of your company culture.

Why identity security matters more than ever.

Every AI integration represents a privileged connection. Whether the AI is generating reports, processing transactions, or directing automated workflows, it likely touches sensitive systems. An identity security approach that prioritizes strong privileged access management (PAM) tools can help control who (or what) gets access, for how long, and under what conditions.

With AI agents making real-time decisions, automating credential lifecycles becomes non-negotiable. PAM solutions built for dynamic environments allow secure provisioning and decommissioning without slowing developers down. By embedding these practices into the enterprise, you can turn identity security into a competitive advantage, not a speed bump.

AI's trajectory shows no signs of slowing. The organizations that win won't just adopt fast, they'll adopt smart. That means putting identity-first security at the core of every AI deployment.

By maintaining visibility, applying adaptive controls, and promoting responsible use, IT teams can support innovation while keeping the business protected. It's not about saying no to AI, it's about helping your organization say yes with confidence.

Learn more about how to monitor, detect, and respond to AI-based threats here.

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