Seemingly overnight AI has evolved, again. AI doesn’t simply generate text, instead AI agents autonomously perform tasks that directly impact business operations. 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.

The New Reality of AI Agents
Unlike traditional AI models, agentic AI systems are equipped with memory, tool interfaces, database connectivity, advanced reasoning, and degrees of autonomy. Agents fall into three primary categories:

• Business agents built on no-code platforms like Microsoft Copilot Studio, Salesforce Agentforce, and ServiceNow.
• Application agents that integrate directly into production systems using frameworks like LangChain.
• Coding agents such as GitHub Copilot accelerate software development.

The sobering reality? These agents are already deployed across enterprises today, whether officially sanctioned or operating in the shadows.

Risk Multiplied
The emergence of agentic AI significantly escalates the risks to business systems compared to genAI. While previous AI risks included sensitive data leakage, model DoS, and hallucinations, AI agents introduce exponentially greater threats. Agent risk includes potential for financial theft, loss of IP, and malicious code accessing sensitive systems as AI agents can now delete files, modify databases, or share confidential information.

Risk associated with agentic AI is influenced by the agent's degree of autonomy, the destructive potential of its available tools, its access to sensitive data, the implementation of runtime guardrails, the inherent security features of the underlying base model, and supply chain vulnerabilities.

A Framework for Secure Agentic AI
Agent security requires a multi-layered approach built on three main pillars:

• Depth and breadth of visibility across your AI landscape and the ability to discover all agents, MCP servers, map data flows, identify third-party integrations and tools, and to document capabilities. Organizations cannot secure what they cannot see.
•  Risk prioritization to focus resources on what matters most by evaluating agents based on business criticality, risk exposure, and compliance requirements. Not all agents pose equal risk, and investments should reflect priority.
• Runtime monitoring enforces real-time guardrails on models and agents in production, detecting and blocking malicious prompts, rogue outputs, and unauthorized agent actions. Ensure your AI agents remain trustworthy in dynamic, highly sensitive environments.

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.

Prepare Your AI Agent Guardrails Now
The question facing CISOs isn't whether agentic AI will transform the business, it's whether the transformation will run with guardrails and cyber safety measures in place. Organizations that take proactive steps today will build a foundation for secure, effective AI that enables innovation while protecting critical assets.

Ready to build your AI agent security strategy? This is just a sample of the soon to be published ebook titled, "The AI Agent Security Playbook: A CISO's Guide to Autonomous AI." Visit Noma Security at Black Hat to get the free download.