Aug 31: AI agents are already operating inside the enterprise. Coding agents write and execute code, interact with repositories, access cloud infrastructure and invoke tools. Workforce agents process documents and messages, retrieve enterprise data and complete business workflows.
As these systems act with greater autonomy, security has to look beyond malicious prompts and individual payloads to the outcomes an agent’s actions can create. Harmful behavior does not always begin with something recognizably malicious. It can emerge naturally from a sequence of otherwise legitimate actions, where the risk becomes visible only in context.
Check Point’s approach brings the full context of the agent’s activity into the security decision.
The protection continuously evaluates the full course of an agent’s activity, including the original user intent, the information the agent has encountered, the actions it has already taken, the policies that apply and the action it is about to execute. It connects those signals in real time and prevents actions that are harmful, unauthorized or inconsistent with the intended task.
This matters because agentic risk often does not appear in a single prompt, response or tool call.
It emerges from context.
Seeing what isolated guardrails cannot
Most AI security controls are designed to recognize a specific type of risk. One may detect prompt injection. Another may identify sensitive data. Another may flag off-task behavior.
These controls remain important, but they depend on knowing what to look for. Check Point’s contextual protection adds a fundamentally different capability: it can recognize harmful agent behavior without requiring a predefined rule or signature for that specific behavior. By understanding the task and the agent’s activity as a whole, it can identify when an otherwise legitimate action creates a harmful outcome in context.
Consider a coding agent working with production systems. It accesses production logs as part of a legitimate task. Later, it stores information from those logs in a working variable. Then it attempts to use that variable while creating a ticket in a public-facing system.
Each step can appear reasonable in isolation.
Together, they create a data leak.
Check Point’s contextual protection understands the relationship between those actions and blocks the final step before the sensitive information leaves its intended environment.
The risk was not hidden inside one action. It was spread across the sequence.
Understanding intent, not just instructions
The same contextual understanding allows Check Point to identify when an agent’s behavior begins to diverge from what the user actually asked it to do.
In one real-world case, an autonomous coding agent operating in auto mode attempted to upload files directly to an S3 bucket. The user had not requested the upload, and the expected development process required the files to move through GitLab.
Uploading a file to S3 is not inherently dangerous. In another workflow, it could be exactly the right action. Here, it was not.
Without relying on a predefined rule or signature for this specific behavior, the protection understood the broader context of the task, recognized that the action did not fit the user’s intent, and prevented the upload before it occurred.
This was not a scripted attack scenario. It was genuine unexpected behavior from an autonomous coding agent during real use.
That distinction captures one of the most important challenges in agent security. Agents do not simply execute predefined commands. They reason about how to complete objectives, choose tools and determine intermediate steps. That flexibility is what makes them powerful, particularly in development environments, but it also creates room for actions that were never explicitly requested and may fall outside the expected process.
A technically valid action can still be the wrong action.
“Agents are useful because they find approaches we did not anticipate. With a ruleset, protection is limited to what we anticipated when we wrote the rules. A security model that evaluates the full task checks each action against what the user asked for and prevents the ones that do not fit. That is what customers are asking us for, and why this capability matters now.”
Ofir Israel, VP AI Security R&D, Check Point
Preventing harmful agent actions
Contextual understanding also adds another layer of defense against indirect prompt injection.
A user may ask an agent to summarize an email, document, webpage or message. Hidden inside that content is an instruction planted by an attacker telling the agent to retrieve credentials, export information or perform some other unauthorized action.
The agent sees both the legitimate content and the attacker’s instructions.
Check Point sees the larger context.
The user asked for a summary. The resulting attempt to retrieve credentials or send information elsewhere does not belong to that task. The protection recognizes the mismatch and prevents the action.
Rather than relying only on detecting the malicious instruction itself, the protection can also recognize the harmful action it attempts to trigger.
The same approach applies when the source of the risk is not malicious.
An agent may attempt a destructive command that has no reasonable connection to its task. It may try to grant itself broader permissions without the required approval. It may attempt to send confidential information to a destination that conflicts with enterprise policy, even when the original instruction came from a legitimate user.
These situations are different in cause, but similar in the way they appear at the point of action. The individual command may be valid. The tool may be approved. The user may be legitimate.
The context reveals whether the action belongs.
From individual signals to the full agent story
The innovation lies in bringing those signals together continuously.
Check Point evaluates the agent’s activity across the flow of work and maintains the context needed to understand how the current action relates to what came before it.
That includes user intent, the information the agent has seen, prior tool activity, the state of the task and applicable policy.
The result is a much richer security decision at the moment the agent is about to act.
Prevention without slowing the agent down
For autonomous systems, the timing of the decision matters as much as the quality of the decision.
Check Point evaluates the intended action before execution and prevents harmful activity while the outcome can still be avoided.
With decisions made in approximately 50 milliseconds, the protection enables real-time enforcement while preserving the speed and productivity users expect from autonomous agents.
The practical result is straightforward.
Sensitive information remains inside the environment. Unauthorized uploads never occur. Destructive operations do not execute. Unexpected permission changes are stopped before they take effect.
The security decision happens at the last responsible moment, with the full context needed to make it correctly.
Built for autonomous AI
Coding agents provide some of the clearest examples of why this capability matters today. They routinely operate across code, repositories, terminals, cloud platforms and production environments, often with enough autonomy to make meaningful decisions about how tasks are completed.
But the same security challenge extends across the broader agent ecosystem.
Enterprise agents are reading email and documents, accessing sensitive business data and interacting with SaaS applications. Multi-agent systems are beginning to pass information and instructions between agents, creating chains of activity where the context of one system can directly influence the actions of another.
Across these environments, the security requirement is consistent: understand what the agent is doing in the context of what it was supposed to do, and prevent the action when those two no longer align.
The protection is already running in production and is being gradually rolled out to Check Point AI Security customers. It will be available across multiple deployment paths, including through Workforce AI Security, native AI Gateway integrations, and direct APIs and plug-ins.