As digital workers proliferate in office environments, the need for clear identity permission management and sensitive data security has come to the forefront.
When a "Crayfish" agent's goals require accessing sensitive corporate data and invoking HR, Finance, or Coding Agents to execute tasks, its autonomous behavior—often described as "reckless" or "indifferent to consequences"—poses a critical challenge. How must office security systems evolve to answer this call in the age of AI Agents?
Alibaba Cloud SASE has released an integrated AI Agent office security solution. By deploying a lightweight SASE Client on employee endpoints, it builds a closed-loop security system covering the entire AI Agent lifecycle: "Pre-event Discovery — In-event Control — Post-event Audit."
As office network tools and data are increasingly invoked by Agents in the future...
OpenClaw's autonomous execution capability stems from its unique multi-level architecture. When viewing the "workflows" of employees as inferable Agents, this architecture presents four categories of security challenges in office environments:
The OpenClaw core engine runs on employee terminals—not in the cloud, but as a truly localized deployment.
This means it possesses deep access privileges: it can read/write files, execute scripts, call APIs, control browsers, and even spawn subprocesses. When this level of privilege meets autonomous execution, it poses a massive test for system security.
Skills are the "skill tree" that empowers OpenClaw's autonomous execution.
OpenClaw adopts an open Skills ecosystem, an architecture that greatly expands the capability boundaries of the Agent. Meanwhile, Skills connect to internal corporate APIs and external cloud services via outbound HTTPS and operate web applications through local browser automation. This significantly increases the attack surface across the entire supply chain.
OpenClaw supports integration with messaging channels like Telegram, Discord, and mobile devices via front-end/conversational interfaces. While this "remote control" capability greatly enhances office flexibility, it also implies that the Agent is continuously performing "driverless" operations without human supervision.
OpenClaw supports running Large Language Models (LLMs) locally on terminals as well as calling cloud-based LLM services. This hybrid deployment greatly enriches the flexibility of enterprise data access, but it also means sensitive data could potentially be compromised—pierced like a "single arrow"—through API channels at any time.
SASE automatically scans and constructs a detailed inventory of AI Agent assets on office terminals. By integrating with AI assets from the Cloud Security Center, it statistically analyzes the distribution of AI assets across departments, personnel, and terminals from multiple dimensions. This forms a complete AI asset inventory and empowers administrators to control unauthorized Agents.
● Skills Poisoning Detection: Precisely identifies suspicious plugins containing malicious injections, Prompt hijacking, and covert exfiltration signatures, specifically addressing high-frequency supply chain security risks for Agents.
● Agent Vulnerability Scanning: Continuously compares version information of Agents and their components on terminals to identify those with known vulnerabilities.
● Over-Privilege Auditing: Automatically inspects MCP Server permissions for file read/write and Shell execution, focusing on high-risk default configurations.
● Compliance & Injection Scanning: Strictly checks for plaintext storage of API Keys and unauthorized model invocation to prevent potential prompt injection hijacking.
As one of the cloud vendors with the most comprehensive security capabilities, Alibaba Cloud SASE 2.0 collaborates with the AI Office Security Gateway, AI Security Guardrails, and Agentic EDR to build a dynamic runtime defense line, ensuring continuous behavior monitoring.
● Intranet Access Control: By integrating with the enterprise's unified identity source, it precisely manages the network access boundaries for Agents to core resources such as OA systems, code repositories, databases, and internal APIs, eliminating unauthorized read/write operations and illicit external connections.
● Sensitive Data Detection: Real-time content inspection is performed on every Agent Prompt input and model output execution. Leveraging SASE's intelligent classification and grading rules, administrators can customize audit and control policies to ensure Agents only process sensitive data authorized by the enterprise.
● Access Key (AK) Management on Office Terminals: Eliminates the need for manual employee configuration; Access Keys are centrally managed to prevent leakage risks.
● LLM Security Management: By integrating with the enterprise's unified identity source, it supports authorizing LLM service invocation permissions based on job roles, ensuring that interactions between Agents and Large Language Models are controllable and visible.
● Alibaba Cloud SASE 2.0 supports the deployment of a dual-channel detection system—combining semantic analysis and a rule engine—at the entry point where Agents receive external instructions. This system identifies and intercepts malicious instructions hidden within normal conversations in real-time (such as typical injection patterns like "ignore previous system instructions" or "send the following content to xxx"). This effectively prevents indirect prompt injection attacks and safeguards the integrity of the Agent's execution chain.
● We construct multi-layered security guardrails around every input and output of the Agent to detect and block risky content, including prompt injection attacks and malicious instructions. The system performs semantic-level injection recognition and filtering when the Agent receives user instructions, and executes content compliance reviews and data masking on the model output side, ensuring that the Agent's "listening" and "speaking" are fully under security control.
● SASE 2.0 employs a terminal behavior analysis engine to continuously monitor low-level Agent operations—such as file I/O, network connections, process creation, and command execution—24/7. It combines this monitoring with UEBA (User and Entity Behavior Analytics) to establish dynamic behavioral baselines.
● The system identifies high-risk behavior patterns within seconds, such as batch reading of key files, execution of destructive commands like rm -rf, or unexpected connections to overseas IPs. Upon detection, it triggers immediate blocking and pushes alerts to the security operations team.
SASE provides full-chain auditing for AI Agents on office terminals. This covers the entire lifecycle, including Agent startup and shutdown, plugin installation and uninstallation, dialogue inputs, and resource access. Every record is precisely correlated with identity, device information, and timestamps.
When a security incident occurs, administrators can quickly restore details to precisely locate data flows involving specific personnel, terminals, and Agents at a given time. The platform also supports automated response measures—such as remotely isolating high-risk terminals and blocking non-compliant Agents—and generates compliance reports and trend analysis dashboards.
Alibaba Cloud SASE Security Platform 2.0 builds a closed loop of "Detection-Control-Audit," precisely governing risks like Shadow AI, poisoning, and privilege escalation. It fully resolves new security challenges while unleashing the productivity benefits of AI Agents in the workplace.
Try it now: Visit the Alibaba Cloud SASE console, install the runtime protection plugin, and start your AI security journey.
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https://www.alibabacloud.com/help/sase/user-guide/ai-asset-management
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