AI agent security refers to the controls, policies, and monitoring systems that govern what autonomous AI agents can access, what actions they can take, and how their behavior is audited at runtime. Unlike model-level safety training, AI agent security is enforced at the system layer — between the agent and the tools, APIs, and data sources it can reach — making it reliable and auditable regardless of the underlying model.
Shield Web

Browser AI usage under control.

Discover shadow AI web apps, inspect prompts and uploads, and enforce policy before sensitive data leaves the browser.

Shield Web protects AI usage in browser-based tools with prompt and upload inspection, web-app classification, tenant-aware policy, and event logging for audit and governance.

Employees use browser AI tools outside policy every day.

Most AI activity starts in the browser. Teams paste internal data into AI assistants, upload customer documents, and jump between approved and unapproved AI apps with no consistent control point.

  • No visibility into which AI web apps are being used by whom
  • No policy on prompt submissions and file uploads in the browser
  • No enforceable workflow for warn, justify, transform, or block decisions

Policy enforcement before every browser AI interaction.

Shield Web applies policy where employees actually use AI: in the browser. Qadar classifies the AI app, inspects prompt and upload context, then enforces your policy in real time before data is submitted.

  • Prompt and upload controls: allow, warn, justify, transform, or block
  • Shadow AI discovery for browser AI apps and tenants
  • Full event trail exported to Shield Control for audit and reporting

Qadar Shield Web capabilities

Real-time tool-call interception

Every tool call an agent makes is intercepted at the API layer before execution. Policy runs in real time — no async logging after the fact.

Agent kill switch

Pause or block any autonomous AI workflow instantly from the Qadar control plane. No engineering ticket, no deployment, no downtime for other agents.

Full agent audit trace

Every agent decision is logged: input context, tool call, output, policy outcome, and timestamp. Exportable to your SIEM via webhook or S3.

Human-in-the-loop approvals

For high-risk actions — writing to databases, calling payment APIs, sending external communications — require explicit human sign-off before the agent proceeds.

Per-agent policy rules

Define different policies per agent, per environment, per tool category. Dev agents can be permissive; production agents follow strict controls — all from one config.

Provider-agnostic

Works with agents built on OpenAI, Anthropic, Azure OpenAI, Google Vertex, and self-hosted models. No lock-in, no migration, no new dependencies for your engineering team.

Common questions about Shield Web

What is Shield Web?

Shield Web is the practice of governing autonomous AI agents at runtime — controlling which tools they can call, what data they can access, and under what conditions their actions require human review. It is distinct from model safety (which is the model provider's responsibility) and operates at the infrastructure layer where agents connect to external systems.

What risks does web AI usage create?

Without browser controls, employees can paste confidential data into unapproved AI tools, upload regulated files, and move sensitive context across model providers without visibility. This creates exposure, policy drift, and audit gaps.

How does Shield Web enforce policy?

Shield Web runs as a managed browser extension and policy layer. Each AI interaction is evaluated in context, then allowed, transformed, flagged, or blocked before submission while events are logged to Shield Control.

How is Shield Web different from traditional endpoint security?

Traditional endpoint security protects devices and monitors network traffic. It does not understand AI prompt content, agent tool calls, or the semantic context of an agent decision. Shield Web operates at the application layer — inspecting the intent and action of each agent call — and is designed for the agentic architecture patterns that endpoint tools were not built to address.

Does Qadar work with all AI models and providers?

Yes. Qadar is provider-agnostic and works with OpenAI, Anthropic, Azure OpenAI, Google Vertex, and self-hosted models. Your engineering team continues calling model APIs as they normally would — Qadar operates as a transparent proxy at the gateway layer.

Get a 30-minute technical demo.

Every request is reviewed against your AI surface, control gaps, and rollout goals before the first call.

  • Scoped to your stack, workflows, and risk posture
  • Pilot-first rollout — no platform rip-and-replace required
  • Response from the Qadar team within 48 hours

Requests are reviewed by the Qadar team — response within 48 hours.