AI Agents Already Have Production-Level Power
Developers use Claude Code. Ops teams use Codex. Support uses OpenClaw. Product teams are building custom agents with MCP.
These systems can delete files, push code, query databases, spin up infrastructure, and send outbound emails with little friction.
A single hallucination, misunderstood prompt, or overconfident autonomous action can trigger production-impacting damage before anyone notices.
The Problem
AI tooling has become the new shadow IT surface: powerful, widespread, and largely ungoverned.
The practical failure mode is simple: risky actions execute instantly with no approval checkpoint, no notification, and no reliable accountability.
- Developer asks Claude Code to clean up old files -> wrong directory is deleted.
- Ops engineer asks Codex to fix deployment -> force push lands on main.
- An autonomous scaling agent provisions GPU capacity overnight -> cloud bill spikes by $50K.
- An MCP-connected tool reads customer PII -> no audit trail shows who approved it.
- A custom agent sends a bulk campaign with a broken template -> 100K customers receive incorrect email.
The Solution: Two Products, One Platform
AuthSec combines two complementary controls so organizations can govern AI actions everywhere: Agent Shield for third-party tools and Agent Guard for custom agent workflows.
Both feed a shared risk engine, approval workflow, and audit layer.
AuthSec Agent Shield
Agent Shield is for organizations using third-party AI tools such as Claude, Codex, OpenClaw, Cursor, and Copilot.
It runs as a local binary on each machine and intercepts risky commands from any AI tool before execution.
- Dangerous operations are blocked pending approval: rm, force push, destructive kubectl, and destructive SQL patterns.
- Safe operations pass instantly with no approval prompt.
- High-risk attempts trigger a push notification to AuthSec Mobile.
- Users approve or deny with biometric verification (FaceID or TouchID).
- Every attempt and outcome is captured in the organization audit trail.
How Agent Shield Works
The Shield model is intentionally simple for developers: one install, one login, instant coverage across tools.
Risk evaluation is performed locally to avoid introducing command latency in normal workflows.
- Replaces selected high-risk binaries with shield shims (for example rm, git, docker, kubectl).
- Routes each invocation through a local risk engine.
- Escalates only high-risk actions to mobile approval.
- Allows temporary pause for intentional unguarded work windows.
- Maintains full event history for admins: user, tool, command, score, and decision.
AuthSec Agent Guard
Agent Guard is for teams building custom AI agents, MCP servers, and autonomous workflows.
Developers add a pre-action check call before risky operations, then execute only when approved.
- Agent calls POST /agent/actions/evaluate with action details.
- AuthSec risk engine returns a score from 0 to 100.
- Score 0 to 30 -> auto-approved and logged.
- Score 31+ -> mobile approval flow is triggered.
- Agent receives approve or deny and proceeds or aborts accordingly.
Agent Guard Works Across Frameworks
Agent Guard is language and framework agnostic, so teams do not need to re-platform existing agent stacks.
It works with direct HTTP usage and SDK-level integration patterns.
- Python agents (including custom services and major orchestration frameworks).
- Node.js and TypeScript agents (including Vercel AI SDK style flows).
- Go services and workers.
- MCP servers and MCP-driven tool execution paths.
Organization-Level Visibility and Control
Agent Shield and Agent Guard events converge into one admin console for policy, response, and compliance.
Security and engineering leaders gain real-time visibility without slowing safe development paths.
- Real-time audit log with user, AI tool, action, risk score, status, and timestamp.
- Configurable risk policies by organization and team.
- Approval thresholds based on risk bands: low, elevated, and critical.
- Support for multi-party approval on highest-risk operations.
Risk Policy Model
Organizations define what risky means in their own environment using scored rules and contextual modifiers.
- Delete protection: DELETE patterns with higher score for bulk and PII scope.
- Source control protection: git push --force and similar high-impact commands.
- Production deploy controls with environment-aware score boosts.
- Bulk communication safeguards for SEND_EMAIL style actions.
- Financial operation controls with stronger scoring for sensitive domains.
- Read-only operations can be auto-approved while still logged.
Approval Thresholds
A tiered model keeps low-risk flows fast while enforcing stronger controls for dangerous operations.
- Score 0 to 30: auto-approved and logged.
- Score 31 to 80: single human approval required.
- Score 81 to 100: multi-party approval required.
Why This Matters Now
AI assistants and autonomous agents are now connected to repositories, cloud control planes, internal systems, and customer data.
Governance standards are still immature, but risk exposure is already operational and immediate.
- A large majority of developers now use AI coding assistants daily.
- Agents are increasingly trusted with production-impacting permissions.
- Traditional compliance controls rarely capture AI tool actions end-to-end.
- Enterprises need a policy and approval layer between agents and critical resources.
AuthSec vs. Common Alternatives
Prompt-only guardrails are not enforcement. AuthSec introduces enforceable policy gates with human decisioning.
- Wrong-file deletion: from unrecoverable loss to blocked-until-approved.
- Bad code push: from hope-based prevention to explicit approve or deny.
- Auditability: from no records to complete action logs with risk scores and decisions.
- Policy control: from none to configurable rules, thresholds, and approvers.
- Critical actions: from single-point mistakes to optional multi-party approvals.
Built for Every Stakeholder
AuthSec is designed for leaders who need governance, security teams who need control, developers who need speed, and agent builders who need reusable approval infrastructure.
- CTO and VP Engineering: every AI tool action is governed and auditable.
- Security Team: risky AI operations are blocked without explicit approval.
- Developer: one install, fast safe commands, phone approval for risky actions.
- Agent Developer: add a small pre-action check instead of building approval systems from scratch.
Get Started
Teams can roll out Agent Shield to developer machines and Agent Guard to autonomous workflows in parallel.
The result is immediate: AI tools remain powerful, but now their high-risk actions are accountable.
- Agent Shield quick start: authsec-shield setup --client-id <id>, then login, then install.
- Agent Guard quick start: call guard.check before destructive actions and execute only if approved.
- Use shared policy and audit controls in the AuthSec admin console for organization-wide governance.
