Why Human-Centric IAM is Failing: The Need for Agentic AI Identity Control

How Can Agentic AI Transform Your Business Without Compromising Security?

The rise of agentic AI in businesses is undeniable. These systems, capable of planning, taking actions, and collaborating, are revolutionizing how we work. However, as companies eagerly embrace automation, they often neglect a crucial aspect: scalable security. This oversight is akin to hiring a digital workforce without ensuring they can securely log in, access necessary data, and execute their tasks safely.

Traditional identity and access management (IAM) systems falter under the demands of agentic AI. Designed for humans, these systems cannot cope with the scale at which non-human identities operate, which can be ten times that of human users. Static roles, long-lived passwords, and one-time approvals no longer suffice. For agentic AI to reach its full potential, we need an identity management approach that acts not just as a gatekeeper but as a dynamic control system for all AI operations.

Is Your IAM System Ready for the Agentic AI Era?

Agentic AI doesn't just run software; it interacts with systems like a human user. It authenticates, assumes roles, and calls APIs. Treating these AI agents as simple application features leads to unchecked privilege escalation and untraceable actions. A single agent with too many permissions can swiftly compromise data or disrupt business processes, often without detection until significant damage has occurred.

The inherent static nature of traditional IAM poses a significant risk. It's impractical to assign a fixed role to an agent when its tasks and data access needs can change daily. Effective access management requires moving from one-time permissions to continuous, real-time evaluations.

How Can You Ensure Agentic AI Security Before Production?

Shawn Kanungo, a leading innovation strategist, recommends using synthetic data to test agent workflows and security measures before employing real data. This strategy builds confidence in your identity controls and audit mechanisms in a risk-free environment. Once your policies and logs prove robust in these tests, you can safely move to using real data.

Crafting an Identity-Centric AI Operating Model

Securing your AI workforce demands a new mindset. Every AI agent should be treated as an essential part of your identity ecosystem.

1. Unique, Verifiable Identities: Assign each agent a unique identity linked to a human owner, a specific use case, and a software bill of materials (SBOM). The time of shared service accounts has passed; they're akin to giving out a master key indiscriminately.

2. Session-Based, Risk-Aware Permissions: Move away from static roles to dynamic, session-based permissions. Access should be timely, limited to the task at hand, and revoked upon completion. It's like giving a key to one room for a meeting, not the whole building.

3. Continuous Context-Aware Authorization: Authorization should be an ongoing process, with systems assessing the context of each request in real-time. This approach ensures a balance between security and operational efficiency.

4. Purpose-Bound Data Access: Direct policy enforcement within the data query engine limits an agent's access to its intended purpose. For example, a customer service agent's queries should be restricted to relevant data, preventing unauthorized financial analysis.

5. Tamper-Evident Audit Trails: In an autonomous environment, the ability to audit every action is crucial. All access decisions and queries should be immutably recorded, providing clear, tamper-evident evidence for auditors.

A Step-by-Step Guide to Enhancing Your AI Security

1. Identity Inventory: Start by cataloging all AI and service accounts to identify and correct any sharing or over-provisioning issues.

2. Pilot a Just-in-Time Access Platform: Test a system that provides temporary, scoped access for specific tasks, demonstrating its operational advantages.

3. Enforce Short-Lived Credentials: Replace long-term tokens with short-lived ones and remove static API keys and secrets from your systems.

4. Create a Synthetic Data Sandbox: Test agent workflows and security policies with synthetic data before moving to real data, ensuring your controls are effective.

5. Run Incident Response Drills: Practice responding to security incidents to ensure your team can quickly address access breaches or other security challenges.

Conclusion

The future of business operations with agentic AI cannot rely on outdated human-centric IAM tools. By positioning identity management at the core of AI operations, organizations can scale their AI workforce securely. Implementing runtime authorization, purpose-specific data access, and rigorous validation using synthetic data are key to mitigating security risks.

Michelle Buckner, with her experience as a NASA Information System Security Officer (ISSO), underscores the importance of evolving our security practices to keep pace with the advancements in AI technology.