The Rise of Shadow AI: How to Manage the Risks of Ungoverned AI Adoption

Read full article from Delinea here:  https://delinea.com/blog/navigating-growing-threat-ungoverned-ai-adoption/?utm_source=nhimg

As artificial intelligence continues its rapid adoption across enterprises, a hidden challenge is rising in parallel: Shadow AI — the unsanctioned or unmonitored use of AI tools and agents outside IT and security oversight. This growing issue is reshaping enterprise risk postures, forcing CISOs to rethink how they govern identity, access, and data security in the age of intelligent automation.

According to Delinea’s 2025 report AI in Identity Security Demands a New Playbook, 44% of organizations admit that business units are deploying AI solutions without IT or security involvement. A similar percentage also reports unauthorized generative AI usage by employees. This uncontrolled expansion of AI capabilities introduces severe governance, compliance, and data exposure risks — particularly as these tools interface with sensitive information systems and APIs.

The Governance and Visibility Gap

Most enterprises recognize the urgency of establishing AI oversight, with 89% implementing some policies or controls to limit access to sensitive data. Yet, only 52% have comprehensive frameworks in place. Common measures such as acceptable-use policies (57%), access controls for AI agents (55%), and AI activity logging (55%) are inconsistently applied, while only 48% enforce identity governance for AI entities. This inconsistency leaves organizations effectively “blind” to where and how AI systems are interacting with critical data, opening the door to compliance failures and insider threats.

The Rise of Agentic AI and Autonomous Risk

The emergence of Agentic AI — intelligent systems capable of autonomous decision-making and self-directed actions — introduces a new class of operational risk. As organizations empower AI agents to act on behalf of users, systems, or workloads, identity boundaries blur. This evolution amplifies attack surfaces and elevates the potential for misconfiguration, privilege escalation, or unauthorized access. Without adaptive identity frameworks, organizations risk losing control over how machine identities are authenticated, authorized, and audited in AI-driven environments.

The Confidence Paradox in Machine Identity Security

Despite visible governance gaps, 93% of organizations express confidence in their ability to secure machine identities. Yet most depend on basic identity lifecycle processes (82%) rather than automated, context-aware controls (58%). Even more concerning, only 61% have full visibility into all machine identities for compromise monitoring. This misplaced confidence creates a dangerous disconnect between perceived security maturity and actual operational resilience.

Building a Framework for Responsible AI Security

To safely embrace AI’s potential, organizations must implement holistic AI identity and governance programs. Key pillars include enforcing acceptable-use guidelines, applying dynamic access controls, maintaining comprehensive logging and auditing, and extending identity governance to AI agents and models. Integrating these controls into existing IAM, PAM, and ITDR ecosystems ensures that AI adoption aligns with enterprise compliance and risk management objectives.

As Agentic AI systems become more embedded in business processes, identity strategies must evolve toward continuous verification, least-privilege enforcement, and automated remediation. Security leaders should also adopt advanced analytics to monitor AI behavior in real time — flagging anomalies, tracking credential usage, and preventing privilege drift.

The Path Forward for CISOs

For CISOs, addressing Shadow AI is not only about containment but about enabling innovation securely. Collaboration between IT, security, and business stakeholders will be critical in defining boundaries and establishing governance-by-design. By aligning AI adoption with identity-centric controls, organizations can unlock innovation responsibly, reduce risk exposure, and maintain compliance across hybrid and multi-cloud environments.

Ultimately, Shadow AI represents both a governance challenge and an opportunity. Enterprises that establish visibility, accountability, and adaptive identity controls will not only mitigate emerging AI threats but also position themselves at the forefront of secure, intelligent innovation.