As organizations deploy autonomous AI agents to handle customer service, software development, and business operations, a new security vulnerability has emerged: these agents can leak sensitive data, fall victim to prompt injection attacks, or consume excessive computing resources without human oversight. KnowBe4, a security platform trusted by more than 70,000 organizations worldwide, has launched Agent Risk Manager to address this gap by monitoring agent behavior in real time, governing what agents can do, and flagging actions that could lead to security incidents .

What Is the "Agentic Paradox" in AI Security?

The challenge facing security teams today is straightforward but complex: organizations need to give AI agents enough access and autonomy to complete their assigned tasks effectively, but not so much that they become a backdoor for data theft, fraud, or system disruption. This tension is what KnowBe4 calls the "agentic paradox." Unlike earlier AI security tools that focused on protecting model access or scanning code, Agent Risk Manager takes a different approach by monitoring what agents actually do after they are deployed in a live business environment .

The shift from AI-assisted workflows to agent-managed workflows represents a fundamental change in how businesses operate. In AI-assisted scenarios, humans remain in control and make final decisions. In agent-managed workflows, autonomous systems interact with web environments, execute code, and access sensitive data with minimal human intervention. This transition has created what security experts describe as a new form of shadow IT, where AI agents operate outside formal oversight and governance structures .

"The industry has spent years securing the human element, but today, AI agents are the newest members of our workforce. However, securing the prompt is only half the battle. Our Agent Risk Manager focuses on the output and actions of these agents, ensuring that as they move through your network, they do not become the ultimate shadow IT or a backdoor for sophisticated prompt injection attacks," said Greg Kras, Chief Product Officer at KnowBe4.

Greg Kras, Chief Product Officer at KnowBe4

How to Secure Autonomous AI Agents in Your Organization

Agent Risk Manager includes several layers of protection designed to govern agent behavior and detect anomalies. The platform combines real-time monitoring, identity governance, and adversarial testing to create a comprehensive defense system:

• Behavioral Guardrails: Real-time monitoring of agent actions to prevent unauthorized data exfiltration or jailbroken autonomous execution, ensuring agents stay within safe operating parameters based on 15 years of behavioral data.
• Prompt Injection Detection: Machine learning-powered analysis that identifies jailbreaks, logic overrides, and indirect injections across user messages and tool outputs, protecting agents from manipulation attacks.
• Sensitive Information Detection: Uses more than 20 classifiers to scan for personally identifiable information and credentials, automatically redacting sensitive data before it reaches audit logs.
• Unbounded Consumption Detection: Monitors for resource abuse and "runaway" agents to prevent excessive API calls, data queries, and compute costs that could indicate a compromised or malfunctioning system.
• Agent Inventory and Identity Governance: Automatically catalogs agents and tools across customer environments and identifies which permissions and tools each agent can access, enabling fine-grained access control.
• Adversarial Simulation: Stress-tests AI agents against the latest prompt injection and social engineering tactics used by hackers to identify vulnerabilities before they can be exploited.
• Audit Log: A filterable, compliance-ready record of all agent actions and detections designed for rapid incident response and forensic review.

The product targets specific risks that security teams have identified as critical. Unauthorized sharing of sensitive information ranks high on that list, as does prompt injection, a technique where attackers manipulate agent inputs to override intended behavior. Excessive use of computing resources is another concern, as compromised or poorly configured agents can trigger runaway API calls that drain budgets and degrade system performance .

Why Is This the Right Time for Agent-Focused Security?

The timing of Agent Risk Manager's launch reflects a broader shift in how cybersecurity vendors approach AI risk. Earlier waves of AI security tools focused on model access, code scanning, and API protection, essentially treating AI as a static asset to be locked down. The new generation of tools recognizes that AI risk is increasingly dynamic and behavioral, driven by what agents do once they are connected to business systems and given access to real data and tools .

KnowBe4 frames this evolution as a move from "human risk to universal risk." The company notes that whether a human is tricked by a deepfake or an AI agent is manipulated by a malicious prompt, the underlying security challenge is similar: preventing unauthorized actions and protecting sensitive information. This unified perspective allows security teams to apply consistent governance principles across both human and machine actors in their organizations .

"We are moving from a world of human risk to universal risk. Whether it is a human being tricked by a deepfake or an AI agent being manipulated by a malicious prompt, KnowBe4 is the only platform capable of defending both," Kras stated.

Greg Kras, Chief Product Officer at KnowBe4

The product is being introduced as part of KnowBe4's HRM+ platform, which focuses on managing risks linked to employees and AI systems. The launch aligns with Identity Management Day, which this year emphasizes the urgent need to secure not just human identities but also the agentic identities of AI systems. Agent Risk Manager will be available globally, though the company has not disclosed pricing .

For organizations deploying autonomous AI agents, the message is clear: governance cannot stop at the prompt or the code. It must extend to the actions agents take in production environments, the data they access, and the tools they use. As AI becomes increasingly embedded in business operations, the security perimeter expands to include machine actors alongside human employees .