AI agent security is knowing which AI agents run in your company, what they can reach and what they do, and stopping the actions that put your data and systems at risk, as they happen.
Autonomous is one platform for it. Its engine discovers every AI agent and what it connects to, protects agents at runtime with guardrails and data loss prevention, and gives them governed access to tools, wherever they run: on laptops, in the browser, in AI SaaS, behind a gateway or in the cloud.
Environments are where AI agents run, each covered by a security layer. The Autonomous Engine turns what those layers see into posture, protection and secure connections, and the Operation Layer hands it to your team and tools. Each box leads to where it is explained.
AI agents don't just answer questions. They read files, run commands, call tools over MCP and reach the systems behind them, with the developer's own access. When a task hits a wall, a helpful agent finds a way around it.
A real session
Asked: How many open pull requests are in this repo?
The unauthenticated API call failed, and so did the browser. The agent then inspected the git credential helpers, read ~/.netrc and pulled a GitHub personal access token out of the macOS keychain to authenticate.
Nobody asked it to. That is the gap: endpoint and network tools see a process and its traffic, not the prompt that led to it, and a gateway sees only the calls that pass through it. AI agent security watches the agent itself, and acts before the command runs.
Install the endpoint agent with one command or silently through your MDM, add the browser extension, and connect your AI SaaS, gateways and cloud agents. About 10 minutes to integrate.
The platform maps the AI agents in use, more than 50 supported, with the MCP servers, skills, extensions and packages they load, exposed secrets and personal accounts. Risk becomes one graded posture and one list of findings.
Decide what is allowed, watch what your policies and guardrails would do, then enforce: dangerous commands blocked before they run, secrets redacted before they leave, unapproved MCP tools stopped mid-workflow.
Three pillars over one policy engine: what agents are and may do, what they are doing now, and what they may connect to.
Discover Shadow AI, assess risk across MCPs, skills, and other assets, and govern what's allowed.
Discover every AI agent, application, model, account, and component in use across your organization. Every AI app, its accounts (corporate or personal), and the skills, MCP servers, commands and extensions it loads, rolled up per person so every risk has a name attached.
Continuously assess MCP servers and skills for vulnerabilities, malicious behavior, risky permissions, and exposed secrets. Paste a repository, package or image and get a scored report: vulnerabilities, malicious code, data exposure, the endpoints it calls. Skills are scanned for prompt injection and embedded secrets.
Define what is approved, restricted, or prohibited and apply those policies consistently across the organization. Allow, restrict or block AI apps, models, organization IDs, MCP servers and single tools, skills and web AI apps, scoped to teams through your identity provider's groups.
Monitor agent behavior in real time, enforce AI-powered and deterministic guardrails, and keep sensitive data within approved boundaries.
See what agents are doing in real time, including prompts, tool calls, commands, data access, and actions. Each session's prompts, tool calls and results, scored for risk, with a deep scan on demand and the full enforcement log.
Fast AI-powered detection with deterministic real-time enforcement. Built-in detectors for prompt injection, secrets, PII, exfiltration, hidden encodings and dangerous commands, run on prompts, tool calls and tool results as they happen.
Automatically restrict agentic workflows when sensitive data is accessed. Name your sensitive sources and trusted destinations. Once a session reads sensitive data, it can send only to destinations you approved.
Give agents controlled access to tools and systems with managed identities, secure gateways, and a token vault.
Control which users and agents can access connected systems and apply access based on identity and context. Who acted, from your identity provider, device manager and the AI apps themselves, so policy applies to people and teams, and each connection signs in as its user.
Secure and govern agent connections to tools, models, APIs, and MCP servers through a controlled access layer. MCP servers run in isolated sandboxes behind one governed endpoint, each tool allowed or blocked for the whole organization, every connection audited.
Keep credentials out of agents by securely storing tokens and providing access without exposing the underlying secrets. OAuth tokens live in a dedicated vault and are injected only when a tool call needs them, so the agent, its prompt and its history never hold the secret.
Our research team studied over 100,000 real AI agent sessions on enterprise endpoints, from Cursor and Claude Code to fully autonomous agents.
Source: The Hidden Security Risks in 100k Enterprise AI Sessions
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Each environment is covered by a security layer: an agent or extension where AI runs, an integration with the AI vendor, or a gateway between agents and the tools and models they call.
A lightweight agent on macOS, Windows and Linux that discovers AI agents such as Cursor, Claude Code, Codex and ChatGPT Desktop, records their sessions and enforces guardrails inline where the agent exposes hooks. Installed with one command or through Microsoft Intune, Jamf Pro, Iru (Kandji), JumpCloud or another MDM.
For AI used in a browser tab, which a desktop agent can't see: ChatGPT, Claude, Gemini, Microsoft 365 Copilot and NotebookLM. It applies the same guardrails at submit time and can block AI web apps you haven't approved.
Claude Enterprise, ChatGPT Enterprise and Microsoft 365 Copilot usage, read from each vendor's own compliance API, including what never touches a managed machine. With Claude Enterprise, guardrails can block a prompt before it reaches the model.
The MCP servers your agents use, run by us in isolated sandboxes instead of on laptops, from a vetted catalog. An administrator adds each server once and decides which of its tools everyone may call.
Already run a LiteLLM proxy? It asks Autonomous to check each request and answer, and lets it through, masks the secrets in it or blocks it, by the same guardrails policy your endpoints use.
AI agents that run in the cloud: dev containers, CI runners and agent sandboxes on Docker and Kubernetes, and the environments Anthropic and Cursor start for Claude Code and Cursor Cloud agents. Each reports as an endpoint, with the same inventory, policy and guardrails.
The Operation Layer: what the platform finds reaches your team where it works, and the tools you already run.
A graded posture, one findings worklist to triage, every session and policy, with role-based access.
A REST API to pull findings and push triage back, and a webhook that posts each finding as it appears or changes.
Audit logs stream to Splunk, IBM QRadar, OpenTelemetry, Syslog or Coralogix, and findings reach your orchestration tools through the webhook.
Real-time alerts to Slack or any webhook, filtered by kind and severity.
It is security for the AI tools that act, not only chat: coding assistants, desktop agents and cloud agents that run commands, read data and call tools. It covers three things: seeing which agents and tools are in use, stopping dangerous actions as they happen, and controlling what agents can connect to.
A gateway sees only the traffic that passes through it. An agent also reads files, runs shell commands and finds other ways out, which happen on the machine. Autonomous watches the agent there too, and its gateways and LiteLLM guardrails cover the traffic in between. More on LiteLLM guardrails.
More than 50 on the desktop, Cursor, Claude Code, Codex, ChatGPT Desktop and Gemini CLI among them; ChatGPT, Claude, Gemini, Microsoft 365 Copilot and NotebookLM in the browser; and Claude Enterprise, ChatGPT Enterprise and Microsoft 365 Copilot through their APIs. Finding the ones nobody approved is Shadow AI.
About 10 minutes to integrate. The endpoint agent runs on macOS, Windows, Linux and containers, in the cloud too: install it with one command, or silently through your MDM (Microsoft Intune, Jamf Pro, Iru (Kandji), JumpCloud, Mosyle, Fleet or any other). Its first scan inventories a machine's AI agents within a few minutes.
Not unless you choose to. Every new policy and guardrail starts off or in monitor mode, where activity is evaluated and logged but nothing is blocked. You move a policy to enforce when its findings look right, for a pilot group first.
Yes. Users and groups sync from Okta or Microsoft Entra ID, and devices from CrowdStrike Falcon, Microsoft Defender or your MDM. Audit logs stream to Splunk, IBM QRadar, OpenTelemetry, Syslog or Coralogix, and findings reach Slack or any webhook.
By default, the prompts, tool calls, responses and session metadata of the AI agents, for audit and investigation. You can set prompts and tool calls to keep only the verdict and metadata, only flagged activity, or nothing, and turn the other kinds off; a block is always recorded. Personal accounts can stay private: collect in full from your approved organizations only, and from personal or unknown accounts we collect metadata alone, their prompts and transcripts never leaving the machine. Seeing the content takes a role that allows it, and the browser extension hashes conversation text rather than storing it.
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