AI Agent Security Platform
The AI agent security platform for what your coding agents actually do — not cloud security, not endpoint security.
One local-first record of agent activity powers six capabilities: runtime monitoring, MCP and skill scanning, compliance-ready audit trails, session evaluation and on-premise deployment. App monitoring extends the same watch-only approach to the applications you ship. Everything stays on the machine that ran it — no telemetry by default, no cloud dependency to trust.
Platform capabilities
Each capability reads from the same event record — nothing here is a bolted-on module with its own agent or its own storage.
AI agent monitoringAI agent monitoring for coding agentsAI agent monitoring means recording what an AI agent does while it runs — every command, file write, tool call and endpoint — and flagging the risky ones with evidence attached. Beam does this locally. Workspace owners and admins can also set observe, advisory, or enforce policy so matching Claude Code and Codex tool calls are denied, and Sentinel can pause or block high-risk browser AI sends.Read more →- App monitoringApp monitoring for attacks in logs and dependenciesApp monitoring watches a running application's logs, HTTP traffic and LLM features around the clock, and flags attacks such as injection, path traversal, reverse shells, prompt injection and attempts to extract API keys. It runs as the open-source beam CLI beside your app or as an SDK inside a Node.js or Next.js app, and it never blocks requests. Detection is heuristic, so findings are leads to review, not verdicts.Read more →
Pre-run scanningMCP security scanningAn MCP server's tool descriptions are instructions your AI will follow. Beam scans an mcp.json before you connect it — 11 heuristic patterns plus a version-pin check for tool poisoning, rug pulls and credential exfiltration.Read more →
Skill scanningCoding assistant securityA skill file is plain Markdown your assistant reads and obeys, which makes it an instruction channel with no review step in front of it. Beam scans one before you run it, reading the file as inert text.Read more →
Audit evidenceAI audit trail for agent activityBeam turns the AI activity you instrument into one timestamped, redacted, exportable record — so a Claude Code session and a Cursor session read the same way. It is audit evidence, not a certification.Read more →
Session scorecardsAgent evaluationBeam turns the activity it already records — commands, file edits, tool calls, flagged findings — into a per-session scorecard: did this agent run stay inside policy, and where did it not. Evaluation against your rules, not a benchmark of model quality.Read more →
On-premise deploymentOn-premise deploymentBeam's collector already runs on the endpoint and writes to local disk by default — nothing to change there. For teams that also want the fleet dashboard inside their own network, it's a Next.js app backed by Postgres that you deploy and own, so agent activity never has to reach Beam's cloud.Read more →
Built for who's actually running agents
Beam is scoped to AI coding agents and the tools they call — not workloads, not cloud config, not your broader endpoint fleet. See how security and IT teams use it day to day.