In our previous post on AI agents, we provided some foundational explanations for understanding agentic AI. In this post we will explore a more practical approach to securing agents within the enterprise.
Seemingly overnight AI has evolved, again. AI doesn’t simply generate text, now AI agents autonomously perform tasks that directly impact business operations. When AI can delete files, modify databases, or share sensitive information, the potential for harm multiplies exponentially.
This shift from passive AI tools to active agents represents both a business opportunity and a security challenge that demands the attention of CISOs everywhere.
Why Agentic AI Introduces New Risks
Several factors determine the risk level of an AI agent, these risk factors include:
Autonomy Level: The degree of independence granted to an agent directly correlates with risk. Fully autonomous agents making irreversible decisions pose greater risks than those requiring human approval for critical actions.
Tool Permissions: The nature of available tools significantly impacts risk. Read-only database access presents lower risk compared to tools that can delete records, modify critical data, or execute system commands. The principle of least privilege becomes crucial when designing agent capabilities.
Sensitive Data Access: Agents with access to personally identifiable information (PII), financial records, or intellectual property require stringent security controls. The broader the data access, the higher the potential for data breaches or compliance violations.
Runtime Guardrails: The presence and effectiveness of real-time monitoring and intervention mechanisms can significantly mitigate risks. Guardrails that detect and prevent harmful actions before execution are essential for safe agent deployment.
Underlying Base Model: The choice of foundation model impacts security. Models vary in their susceptibility to prompt injection, hallucination rates, and alignment with safety principles. Understanding these characteristics is crucial for risk assessment.
Supply Chain Vulnerabilities: The agent ecosystem includes numerous dependencies – MCP servers, tool libraries, integration frameworks – each potentially introducing vulnerabilities. A compromised component in the supply chain can undermine the entire agent’s security posture.
How to Secure Agentic AI
While the answer to this isn’t necessarily simple, the same principles apply to securing AI agents as with any other type of AI within the enterprise. 1. Broad and deep contextual understanding of the AI agents within the organizational environment 2. Proactive security measures to manage risk 3. Ensuring agent interactions are monitored at runtime. In other words, securing agentic AI requires a comprehensive, multi-layered approach to ensure safe, secure and compliant use.
1. Visibility: Know Your AI Landscape
The first step in securing agentic AI is understanding what exists within your organization. This includes discovering and inventorying all AI agents, models, and tools in use, mapping data flows and access permissions, identifying MCP servers and third-party integrations and documenting agent capabilities and autonomy levels. For example, an organization utilizes a business agent to launch an HR chat bot that has access to employee information and the ability to provide answers to sensitive questions. Without comprehensive visibility, shadow AI deployments can proliferate, creating unmanaged risk exposure. This can be a lot of work if done manually, which is why AI agent security solutions can be helpful.
2. Risk Prioritization: Focus on What Matters Most
Not all agents pose equal risk. In order to minimize risk effectively, consider prioritizing risk in the following ways:
- Business Criticality: Agents handling core business processes or customer-facing operations demand higher security attention. For example, a customer support agent designed to help users with account inquiries should not have unrestricted access to the entire customer database to avoid a misconfigured agent inadvertently exposing sensitive information like social security numbers to other customers.
- Risk Exposure: Evaluate based on data sensitivity, tool capabilities, and autonomy levels. Referring to the previous HR chatbot example, ensuring the agent does not have access to sensitive information such as payroll, so not to risk exposing this information.
- Compliance Requirements: Agents processing regulated data (HIPAA, GDPR, SOX) require additional controls and monitoring to ensure regulatory compliance.
3. Process and Governance
Agent security should be continuous, especially as they become more embedded and accessible into day-to-day business operations. You must establish robust processes for agent lifecycle management, including:
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Pre-deployment Audits: Review agent design, permissions, and potential risks before production deployment;
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Continuous Monitoring: Implement real-time monitoring of agent actions and decisions;
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Regular Security Reviews: Periodically reassess agent configurations and access rights;
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Incident Response Plans: Develop specific procedures for agent-related security incidents;
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Implement Runtime Guardrails: Enhance protection with built-in guardrails that limit unauthorized agent access and prevent sensitive data leakage and tool abuse.
Blocking organizational AI adoption isn’t a viable option and will simply push usage into the shadows. Secure AI and agent adoption by providing guardrails and fostering a culture of responsible AI innovation.
Enforcing Secure AI Use
Attempting to block all AI use is not only impractical but counterproductive. When organizations implement overly restrictive policies, employees don’t stop using AI, they simply use it without oversight. This shadow AI usage creates blind spots in security posture and prevents organizations from implementing proper controls.
The key to secure AI adoption lies in enablement, not restriction. By providing secure, approved pathways for AI use, and the build out of agentic AI platforms, organizations can harness the benefits of AI agents while maintaining security and compliance.
Conclusion
The rise of AI agents represents a fundamental shift in how we interact with artificial intelligence. As AI systems evolve from passive text generators to active agents capable of autonomous action, our approach to AI security must evolve accordingly.
Success in this new landscape requires a balanced approach: comprehensive visibility into AI usage, thoughtful prioritization of risks, robust governance processes, and a culture that promotes secure innovation over shadow IT. Organizations that embrace this challenge, investing in proper controls while enabling their teams to leverage AI effectively, will be best positioned to realize the transformative benefits of agentic AI while managing its inherent risks.
The question is no longer whether agentic AI will transform your organization, it’s whether that transformation will happen under your security team’s watchful eye or in the shadows. The time to act is now, before autonomous agents become so deeply embedded in business processes that securing them becomes exponentially more complex. By taking proactive steps today, organizations can build a foundation for secure, effective use of agentic AI that drives innovation while protecting critical assets and data. To learn more about how Noma Security can help you implement security for AI Agents and embrace this technology with confidence, schedule a demo for you and your team.
