AI security has been treated as a prompt problem. But the real risk is execution: files, network, processes, and secrets. Execution-Layer Security enforces least privilege at runtime, across supervised copilots and unsupervised agents.
Prompt-time defenses are important, but they are probabilistic. Even a well-aligned model can be tricked, misled, or simply take the wrong step. When AI can execute — access files, open network connections, run commands, read secrets — security needs deterministic controls where actions happen, not just where instructions are parsed.
Malicious instructions embedded in data can hijack agent behavior. Controls at the execution layer limit what a compromised agent can actually do.
Agents call the right tool with the wrong arguments — or the wrong tool entirely. Runtime policy catches actions that don't match intent.
Overly permissive settings, stale credentials, and forgotten access paths. Execution-layer enforcement applies least privilege regardless of configuration drift.
Execution-Layer Security is the runtime enforcement and audit layer that governs what AI workloads can do: files, network, processes, and secrets — independent of prompt compliance.
Policies evaluate actions at the moment they happen — not after the damage is done. Every file access, network call, and command is checked before it executes.
Every action gets a clear verdict: allow, prompt (human-in-the-loop), block, or redirect to an approved alternative. No ambiguity, no silent failures.
Decisions include the tool that triggered the action, the full process chain, destination, command, and scope — so security teams can reconstruct exactly what happened.
Every decision is logged with full context and exportable to your SIEM (Splunk, Sentinel, QRadar) or OpenTelemetry pipeline for compliance and forensics.
Works the same regardless of which model, framework, or AI tool triggered the action. Claude, GPT, Gemini, LangChain, CrewAI, custom agents — enforcement happens at the OS and network layer, not the model layer.
Redirect actions to approved alternatives instead of hard-blocking. Dependency installs go to internal mirrors, API calls route through approved gateways — agents stay productive.
Copilots and desktop AI tools operate with user permissions. "Human in the loop" does not guarantee safety unless the system can enforce policy at runtime — controlling what tools can access, connect to, and execute.
Learn about BeaconHeadless agents execute without UI prompts. They need deny-by-default capabilities and explicit allowances — for network, filesystem, commands, and secrets.
Learn about AgentSHMonitors and controls supervised AI on employee endpoints — macOS and Windows. Per-app visibility, runtime policy, and human-in-the-loop approvals for desktop AI tools like Claude, Cursor, and ChatGPT.
Learn about Beacon →Wraps unsupervised AI agents in CI, containers, and dev environments. Enforces least-privilege policy at execution time — network, filesystem, commands, and secrets. Open source.
Learn about AgentSH →The command center: central policy management, approval routing, SIEM export, fleet-wide kill switch, and RBAC. Governs both Beacon and AgentSH from one place.
Learn about Watchtower →We complement red-teaming, evals, and safe prompting by bounding blast radius when they fail.
EDR and DLP are critical, but they were built for humans and apps, not machine-speed agent execution. Execution-Layer Security adds AI-aware context and enforcement at the moment of action.
Isolation is necessary but not sufficient. Execution-Layer Security is about policy-driven capabilities, not just "inside vs outside".
Hard blocks often cause agents to retry, escalate, or route around restrictions. Steering keeps teams productive: redirect dependency downloads to approved registries, route model calls to approved gateways, and constrain endpoints without breaking workflows.
Instead of blocking npm install or pip install, redirect dependency downloads to your internal mirrors and approved registries. Agents keep working; supply chain stays clean.
Redirect outbound API calls to approved domains and gateways. Model calls go through your proxy, data stays in approved channels, and shadow endpoints get caught.
For high-risk commands like deploy, credential access, or database mutations — prompt a human for approval instead of blocking. Keeps automation flowing with a safety net.
No. Prompt injection is one cause of unsafe execution. Execution-Layer Security reduces risk from any failure mode: injection, mistakes, tool misuse, misconfiguration, or compromised dependencies.
Beacon secures supervised AI workloads on endpoints. AgentSH secures unsupervised agentic workloads in CI, containers, and dev environments.
Watchtower is the command center: central policies, approvals routing, SIEM export, and a fleet-wide kill switch. Beacon and AgentSH enforce locally at execution time.