Securing Claude Code: Managing AI Agent Risk with Ceros Visibility

The New Frontier of Non-Human Identity Risks

Security teams have traditionally focused their Identity & Access strategies on human users and service accounts. However, the rapid adoption of AI coding agents has introduced a new class of actor into the enterprise environment. Unlike traditional LLMs that provide passive text completion, agents like Claude Code by Anthropic are designed to interact directly with the local file system and development environment.

This shift toward agentic workflows means that these entities can perform actions that closely resemble the TTP of a malicious actor if not properly governed. According to The Hacker News, the core challenge lies in the fact that these agents operate entirely outside the standard controls established for human engineers. As these tools scale across engineering organizations, the risk of unmonitored RCE-like capabilities increases, making it difficult for the SOC to differentiate between legitimate automated refactoring and unauthorized system modifications.

Claude Code Security Risks and Agentic Autonomous Actions

Claude Code is capable of reading sensitive configuration files, executing shell commands, and calling external APIs to facilitate software development. While these features drive productivity, they also present significant Claude Code security risks. Because the agent inherits the permissions of the developer who executes it, a compromised or misaligned agent could facilitate Privilege Escalation or inadvertent data exfiltration.

The lack of native attribution in existing EDR and SIEM tools creates a blind spot. When Claude Code executes a command like rm -rf or initiates a network connection to an external library, the telemetry often appears as if the human developer performed the action. This obfuscation makes it nearly impossible to conduct accurate forensic analysis during an incident or to identify an APT that might be leveraging AI-native tools for Lateral Movement.

How to monitor AI coding agents and ensure governance

To address this visibility gap, the Ceros platform provides a dedicated security layer designed to intercept and analyze agentic actions. Understanding how to monitor AI coding agents requires a shift from infrastructure-centric monitoring to semantic-aware monitoring. Ceros acts as a proxy or observer that logs the intent and the action of the agent, rather than just the raw system call. This allows security teams to define granular policies for what an AI agent is permitted to do, such as:

• Restricting access to specific sensitive directories (e.g., .ssh or .env files).
• Mandating human-in-the-loop approval for high-risk shell commands.
• Auditing all outbound API calls made by the agent for potential data leaks.

Implementing Defensive Strategies for AI Agents

Defenders should prioritize securing autonomous AI agents in engineering by adopting a Zero Trust approach to non-human identities. Relying on the assumption that an agent is safe because it is running on a trusted developer’s machine is no longer sufficient.

First, organizations should inventory every instance where Claude Code or similar tools are deployed. Second, security teams must integrate agent activity logs into their centralized monitoring systems to detect anomalies. Finally, utilizing platforms like Ceros helps bridge the gap between developer velocity and security requirements, ensuring that the MITRE ATT&CK techniques potentially used by automated tools—such as ‘Execution’ (T1059) and ‘File and Directory Discovery’ (T1083)—are captured and alerted upon in real-time.