Agentic Shield enforces controls on every agent call and produces the audit trail reviewers require — so compliance is something you demonstrate, not just assert.
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AI agents aren't chatbots. They take action on their own — calling tools, reaching into your data, and changing real systems without a human in the loop. Agentic Shield is the checkpoint that sits in front of them: before an agent's action reaches your production systems, it decides whether the action is allowed, enforces your policy, and writes down what happened in a record you can prove later.
A new kind of risk that old tools miss. Traditional security hunts for known-bad code and stops it at the network or the machine. An AI agent can be talked into causing harm in plain English, with no malware anywhere — and because it acts on its own, a single manipulated decision can leak data, move money, or break a system directly. Signature scanners were never built to catch that.
You can't prove what you can't see. Most teams running agents today have little real-time insight into what those agents are actually doing, and even less ability to show, after the fact, what happened and why. Regulators, auditors, and enterprise customers increasingly expect that evidence on demand — and “trust us” no longer passes.
One rulebook across every model you run. Real deployments mix models — some in the cloud, some running locally. Guardrails tied to a single provider only watch their own corner and leave the seams between them exposed. Agentic Shield applies the same policy, and keeps one consistent record, across all of them.
Every inbound request is analyzed inline before it reaches an agent, and every enforcement decision is written to a tamper-evident log. You get a defensible record that your controls actually ran, not just that they exist on paper.
Signature-based XSS and SQLi checks run on every call, independent of any model. Reviewers get consistent, explainable enforcement they can test and reproduce — not a black box.
On production accounts, a per-account behavioral baseline learns normal usage and flags activity that deviates — unfamiliar IPs, off-hours bursts, new endpoints — so you can catch and evidence misuse before it becomes a reportable incident.
Every agent gets its own short-lived, individually revocable credential. Access reviews become simple: show exactly which agent can do what, and revoke any single one instantly — no shared keys, no downtime.
Your agents don't all live in one cloud — your controls shouldn't either. Agentic Shield enforces the same policy whether a call routes to Gemini, Hugging Face, or a local Ollama deployment.
Cloud-native guardrails like AWS Bedrock Guardrails and Azure AI Content Safety only cover their own provider's models. A multi-model estate leaves gaps between them — and a separate, incompatible audit trail in each. Agentic Shield sits in front of all of them, so one consistent, auditable policy applies everywhere, with a single trail of evidence to show for it.