product
Shield Web
Intercept browser-based agent actions and enforce policy before tool calls proceed.
AI Agent Security
Your AI agents act without guardrails
Agents make tool calls, access data, and take actions on their own. Qadar AI enforces policy before every tool call, logs every decision, and gives you a kill switch when needed.
The gap
From risk signal to governed action
A Shield-Web-style walkthrough that shows the challenge first, then the control path Qadar AI applies in production.
Challenge
The agent risk
AI agents are gaining access to more tools, more data, and more autonomous decision authority every week. Your security team cannot review and approve every new tool integration at the pace agents are evolving. Without runtime controls, agent risk compounds silently.
Before Qadar AIChallenge
Signal detected
The agent risk
Risk context
AI agents are gaining access to more tools, more data, and more autonomous decision authority every week. Your security team cannot review and approve every new tool integration at the pace agents are evolving. Without runtime controls, agent risk compounds silently.
Qadar AI response
Policy checks before every tool call
Qadar AI enforces policy at the point of every agent tool call. Before an agent reads data, writes to a system, or takes an action, the request passes through policy evaluation. Per-agent rules, tool-use controls, and an agent kill switch give your team runtime authority over agent behavior.
After Qadar AIQadar AI response
Policy decision
Policy checks before every tool call
Governed action
Qadar AI enforces policy at the point of every agent tool call. Before an agent reads data, writes to a system, or takes an action, the request passes through policy evaluation. Per-agent rules, tool-use controls, and an agent kill switch give your team runtime authority over agent behavior.
Capabilities
What AI agent security looks like with Qadar AI
Per-agent policy rules
Define granular policies per AI agent based on risk profile, data sensitivity, and authorized tool access. Different agents get different controls.
Tool-call interception
Every agent tool call is intercepted and evaluated against policy before execution. Unauthorized tool access is blocked with full logging.
Agent kill switch
Terminate agent sessions that exceed policy boundaries. Kill switch controls are enforced immediately with structured incident logging.
Full agent audit trace
Every agent action, tool call, and decision is logged in a structured audit trail. Trace agent behavior across sessions for compliance review and incident investigation.
MCP governance
Govern Model Context Protocol tool access and interactions. Per-tool policies with audit coverage for MCP-connected agent workflows.
Human-in-the-loop gates
High-risk agent actions trigger approval gates. Designated approvers review and authorize before the agent proceeds, with full decision logging.
FAQ
Questions teams ask about AI agent security
Questions teams ask about AI agent security
FAQ
An agent kill switch is a runtime control that immediately terminates an AI agent session when it exceeds policy boundaries. Qadar AI enforces kill switch controls at the browser level with structured incident logging for post-incident review.
Qadar AI provides governance for Model Context Protocol tool access. Per-tool policies control which MCP tools agents can access, what data they can read, and what actions they can take. All MCP interactions are logged in the structured audit trail.
Yes. Shield Control supports per-agent policy configuration. Each agent gets controls scoped to its risk profile, authorized tool access, and data sensitivity requirements.
Related
Go deeper on AI agent security
product
Shield Control
Centralize agent policies, approvals, kill-switch actions, and audit trails.
guide
AI Agent Security Guide
Go deeper on securing agents with runtime controls and traceable decisions.
Book a technical walkthrough focused on runtime agent risk
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