Get your AI agent approved and into production in 30 days

One forwardable Release Packet that gives Security, Legal, and Product everything they need to say yes — so you can ship fast.

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Your agent works. But three teams won't approve it.

Most conversation agents don't fail because the model is bad - they fail because enterprises can't sign off on them.

Security says no

"We can't verify what data it accesses, what tools it can call, or how it handles prompt injection. There's no evidence to review."

Legal says not yet

"We need to know what content it can produce, how PII is handled, and whether it stays within approved topics. We can't approve what we can't audit."

Product says too risky

"One brand incident and we're on the front page. We need guardrails we can prove are working — and a way to debug when something goes wrong."

One artifact that turns "launch risk" into signed approvals

Enkrypt AI produces an Agent Release Packet — a single, forwardable document that gives every stakeholder the evidence they need to approve your agent.

Executive Summary

Top risks, approvals, launch blockers — one page for leadership

Policy bundle

What's allowed, blocked, and requires approval — with policy IDs

Approved Actions Catalog

Every action risk-tiered with approval rules and owners

Data Registry

Connectors + RAG corpus approved or blocked by intended use

Regression Proof

Test cases for jailbreaks, injection, and leakage — with pass/fail results

Audit artifacts

Decision logs, approval records, and incident export schema

What you get on Day 1 vs Day 30

Start building immediately. Ship with full approvals in a month.

Day 1 - Unblock Engineering

Start building immediately

Baseline policies and guardrails so your team doesn't wait.

Policy template + baseline guardrails

Pre-configured for your industry

Actions catalog schema

Risk tiers + approval requirements

Data manifest template

For connectors + RAG corpus

First regression pass

Against your system prompt and top flows

Day 30 — Ship with Approvals

Forward the packet, get sign-off

A complete release packet ready for Security, Legal, and Product.

Complete Agent Release Packet

Go/no-go, approvals, open risks, evidence

Finalized actions catalog

Owners, approvals, environments locked

Data inputs approval packet

DBoM + approved/blocked registry

CI-ready regression suite

Every change gated before production

Five steps from "blocked" to "approved"

Start with one agent and one environment. Repeat for every agent after that.

Six products, one workflow — reuse across every agent

Standardize once. Every new agent becomes a repeatable release, not a new security project.

Guardrails

Runtime

Block, allow, rewrite, or escalate — enforced at runtime with policy IDs and reason codes.

Red Teaming

Pre-launch

Find jailbreaks, injection paths, tool misuse, and data leakage before your users do.

Policy Engine

Governance

Turn governance policy into enforceable controls and a signed policy bundle.

Data Risk Audit

Data

Approve every dataset, connector, and RAG corpus the agent can access.

MCP Scanner

Tools

Scan and risk-rate MCP servers and tools before they're connected.

MCP Gateway

Tools

Inline enforcement for tool traffic with approval receipts and trace logs.

Built for teams that ship to regulated industries

Audit-ready from day one. Plugs into the tools your teams already use.

Integrations

Alerts

Slack / Teams
PagerDuty / Opsgenie

Workflows

Jira
ServiceNow

Security

Splunk/ Sentinel/ Datadog
Webhooks

Exports

JSON/CSV evidence for reviews and retention

Frequently Asked Questions

How does this help us ship faster?

By standardizing policy, approvals, data allowlists, and CI regressions into one repeatable workflow, each new agent becomes a release — not a new security project. Teams that used to spend 4–6 months in review loops ship in 30 days.

Does this support tool-using agents and MCP?

Yes. When your agent uses tools or MCP servers, Enkrypt AI adds MCP Scanner (to risk-rate tools before connection) and MCP Gateway (to enforce policies on tool traffic inline), with approval receipts and trace logs.

How do you prevent data leakage from RAG and connectors?

Data Risk Audit approves every source by intended use. Guardrails enforce redaction and blocking at runtime. Red Teaming validates leakage paths before launch. All three layers work together.

Can we use regressions as a release gate in CI?

Yes. Regressions are designed to run in your CI pipeline so that changes to prompts, tools, models, or data are automatically gated before they reach production.

Do you support voice and multimodal agents?

Yes. Policies apply across text, audio, and vision inputs — including cross-modal prompt injection where an attack in one modality targets another.

Who is this for?

Teams shipping customer-facing chat or voice agents in regulated industries — Finance, Healthcare, Insurance, Government — that must be secure, on-brand, and compliant before they go live.

Launch customer-facing agents faster - with a release packet your enterprise can trust.