We are excited to announce a new solution for securing AI agents. The solution helps organizations gain visibility into their AI agents and manage and secure their access to databases by applying identity security controls such as zero standing privileges.
As organizations look to achieve the efficiency and cost benefits of agentic AI, they are also concerned with the security implications. Unit 42 research indicates that AI agents are rapidly becoming a core component of enterprise operations, with projections that 40% of enterprises will deploy AI agents fully by 2026.
As these autonomous systems become embedded into core business functions, they are redefining the cybersecurity threat landscape by acting as force multipliers for both productivity and, concerningly, cyberattacks.
Security Challenges Of Ai Identities: A New Identity Class
While AI agents can be considered machines by definition, they also show characteristics associated with human identity in their ability to reason, make decisions, and act toward goals. But when you consider their scale and their ability to operate 24/7, they are more similar to machines.
AI agents also inherit the threats associated with both humans and machines. Just like humans, agents can be affected by compromised credentials, excessive privileges, or session hijacking. And like machines, they can inherit risks associated with stolen keys and secrets leakage.
To secure AI agents effectively, enterprise security leaders increasingly recognize the need for an approach that combines powerful controls for securing human and machine identities. Traditional authentication using OAuth tokens and consent flows worked when AI agents performed specific tasks on behalf of someone, but that covers only a small number of use cases.
As autonomous AI agents and multi-agent systems become more pervasive, more complex use cases will function similarly to cloud-native workloads and rely on machine identity authentication methods such as secrets, API keys, and certificates. For organizations building broader controls around autonomy, agentic AI security and agentic AI governance are becoming central considerations.
Secure Ai Agents Capabilities
A secure AI agents approach should address the key areas required for agentic AI security. These capabilities follow the lifecycle of an AI agent. It starts with discovering agents and understanding their context, followed by securing agents and managing their lifecycle and compliance.
Discovery And Context
Before you can secure your AI agents, you first have to understand your environment: what agents are running, who owns them, and what are their potential risks?
A strong discovery program should detect AI agents running across SaaS, cloud, and developer environments. Teams need a clear view of their agents, including statuses such as discovered, active, or pending connection. Each agent should be enriched with context such as ownership, purpose, status, and permissions, helping security teams understand who owns each agent, what it does, and what it can access.
Secure Access
Once you have a view of the agents in your environments, you need to ensure they are secure. AI agents are privileged identities by nature, with access to sensitive resources. Based on established best practices for securing access for human and machine identities, privilege controls need to be applied to an agent before it interacts with SaaS apps, databases, human users, or other resources.
An effective enforcement layer between AI agents and the tools they use can help control how they connect to resources through frameworks such as MCP. That layer should apply identity security controls including zero standing privileges and least privilege access, helping security teams reduce standing access for AI agents. The goal is to ensure permissions are granted only for the specific task, scoped to the intent of that task, and revoked automatically so the agent does not retain unnecessary access.
Lifecycle Management And Compliance
Security leaders need the ability to govern the lifecycle of AI agents and ensure auditability and compliance, especially in regulated industries. That means logging agent actions and communications so security teams can examine what actions were performed, by which agent, and on behalf of which human user. Teams also need visibility into which resources were affected and what queries or actions the AI agent initiated, including ones the associated human user may not fully understand or even be aware of.
These capabilities help organizations secure and manage the end-to-end lifecycle of AI agents. A mature approach allows enterprises to apply the same rigor used for human and machine identity security, while also accounting for the unique behaviors of agent identities. While this approach is designed to scale, it also gives security teams and developers a practical starting point for securing AI agents in their own environments. Agentic AI is not going away, and the time to start thinking seriously about security is now.
For a broader strategic view of how organizations can prepare for this shift, see Autonomous Security Operations: The CISOs’ Guide to Agentic AI for Defensive Parity.