Shadow Ai Identityshield

This presentation at Identity Shield, Pune explores Shadow AI as the newest supply chain disruptor. Anant Shrivastava examines how the rapid proliferation of AI tools across organizations — often adopted without IT or security team oversight — creates a new class of shadow technology risk that intersects with software supply chain security, identity management, and data governance. The talk covers shadow AI risk classification, discovery approaches, and a balanced “carrots and sticks” strategy for organizational adoption, framed within an operational loop for continuous management.

Key Topics Covered

The Changed Landscape:

• The world of software development and IT has fundamentally shifted with the rise of AI-driven tools
• AI tools are being adopted across organizations at a pace that outstrips traditional security and governance processes
• This creates a new category of risk that extends beyond traditional Shadow IT

Shadow AI as a Supply Chain Vector:

• Shadow AI encompasses unauthorized AI tools, models, and services used within an organization without formal approval or oversight
• Unlike traditional Shadow IT (unauthorized hardware or SaaS), Shadow AI introduces unique risks around data leakage to training pipelines, model hallucinations in production code, and unvetted AI-generated outputs entering the software supply chain
• The intersection of Shadow AI with identity management is particularly critical — AI agents acting on behalf of users inherit and potentially expose identity credentials

Types of Shadows:

• Shadow IT: unauthorized technology adoption by individuals or departments
• Shadow AI: unauthorized AI tool usage extending beyond traditional Shadow IT patterns
• Both converge in production environments where unauthorized tools generate code, configurations, or data that enter managed systems

Risk Classification Framework:

• Shadow AI risks are classified across multiple dimensions depending on the type of AI tool, the data it accesses, the outputs it produces, and the degree of organizational integration
• Higher risk when AI tools have access to production data, credentials, or customer information
• Lower risk for isolated experimentation without organizational data exposure

Current Approaches and Their Limitations:

• Traditional approaches to handling Shadow IT are insufficient for Shadow AI due to the speed of adoption and the difficulty of detecting AI tool usage
• Inventory-based approaches require new discovery angles specific to AI tool patterns

Discovery and Inventory:

• Multiple angles exist for discovering Shadow AI usage: network traffic analysis, expense report monitoring, endpoint tooling audits, and API access logs
• Building and maintaining an inventory of AI tools in use is the foundational step for managing Shadow AI risk

The Operational Loop:

• A continuous cycle of discovery, assessment, policy enforcement, and review
• Not a one-time audit but an ongoing operational process that evolves as new AI tools emerge

Carrots and Sticks — Balanced Governance:

• Pure enforcement (sticks) drives Shadow AI further underground; pure enablement (carrots) without guardrails increases risk
• Effective governance combines sanctioned AI tool options (carrots) with clear policies and consequences for unauthorized usage (sticks)
• The incentive loop must align organizational productivity goals with security requirements

Rethinking Security Roles:

• Security practitioners need to re-examine their role in the context of AI-driven development
• The focus shifts from gatekeeping to enabling secure AI adoption while maintaining visibility and control
• Collaboration with development, business, and IT teams is essential for effective Shadow AI governance

Actionable Takeaways

1. Conduct a Shadow AI inventory across your organization by analyzing network traffic, expense reports, endpoint tooling, and API access logs to identify unauthorized AI tool usage.
2. Develop a Shadow AI risk classification framework that accounts for data sensitivity, credential exposure, production integration, and output trust levels.
3. Implement a “carrots and sticks” governance model: provide sanctioned AI tool options with appropriate guardrails while establishing clear policies for unauthorized AI usage.
4. Establish an operational loop for continuous Shadow AI management — discovery, assessment, policy enforcement, and periodic review — rather than treating it as a one-time audit.
5. Redefine the security team’s role from gatekeeping to enabling secure AI adoption, collaborating with development and business teams to balance productivity with risk management.