Syncalytics AI Governance

Enforce guardrails on AI agents so they act within your approved policies and models.

AI is entering workflows faster than companies can control it. Syncalytics AI Governance moves control into the runtime itself: every agent has an identity, every event is evaluated against policy, and every decision leaves audit-ready evidence.

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Runtime guardrails across eleven runtime event types
Identity, approvals, budgets, and audit-ready evidence in one control plane
Version 3.0.0, released July 20, 2026

What It Does

One control plane for governed AI operations.

Syncalytics AI Governance enforces guardrails on how AI agents operate: which agent is acting, what data it can retrieve, what content reaches a model, which tools it can call, which actions need approval, and what evidence remains.

The risk is not only model quality. It is agents with broad credentials, weak runtime checks, unclear approvals, uncontrolled spend, and evidence scattered across logs after something has already happened. Syncalytics fixes the four problems that stall AI on the way to production:

Agent sprawl: copilots, retrieval agents, tool-using agents, and automations get one consistent model for identity, permissions, and ownership
Unsafe runtime behavior: prompts, model outputs, tool calls, RAG context, memory, MCP tools, and agent-to-agent messages are controlled while the event is happening
Approval and audit gaps: high-impact actions pass policy gates and human review, leaving a clean decision trail that risk, security, legal, and operations teams can inspect
Production readiness risk: proof that integrations enforce controls, deployments are ready for scale, and configuration can move safely between environments

The result: your teams ship AI workflows without relying on screenshots, scattered logs, shared keys, or manual follow-up to prove what happened.

Runtime Guardrails

Inspect the event while it is happening. Decide before it proceeds.

Guardrails evaluate the content flowing through an AI agent at runtime and return an enforceable decision. Detection and decision are kept separate, rollout is staged, and sensitive content stays out of storage.

Eleven event types, five decisions

Guardrails cover LLM input and output, tool calls and responses, RAG context before grounding, memory reads and writes, MCP tool calls and responses, agent-to-agent messages, and trace events. Every evaluation returns one of five decisions: allow, block, sanitize, require approval, or log only. One decision vocabulary across providers, frameworks, gateways, SDKs, and custom runtimes.

Detectors as evidence, policies as deciders

Detectors produce severity-scored findings such as PII, prompt injection, or tool abuse. Policies decide what those findings mean for a specific environment, agent, gateway, role, group, framework, or binding. A finding is evidence. A policy is the decision.

Staged rollout, by design

Start with template packs in log-only mode, review findings and decision liveness, simulate enforcement, bind profiles by scope, then move to enforce mode. The platform separates configured from observed enforcing, so you always know what is actually protecting traffic.

Privacy by default

Raw prompt, tool, model, RAG, and memory content is not stored by default. The platform preserves the decision, reason code, findings, attribution, and the evidence needed for review, never the sensitive payload.

Release Highlights

What's new in version 3.0.0 (July 20, 2026)

Version 3.0.0 introduces Runtime Guardrails: an inline, runtime-neutral control layer that inspects the content flowing through an AI agent and returns an enforceable decision. Here is what the release brings.

Runtime Guardrails

Inline content controls for all eleven runtime event types. A single native evaluate endpoint serves every runtime, provider-shaped adapters cover popular clients and gateways, and explicit fail modes govern degraded conditions on both the server and the client side.

Guardrail Template Packs and Readiness Checks

Five recommended packs ship in the product: Data Protection, Secrets Hygiene, Prompt Injection Defense, Tool Abuse Control, and Log-Only Starter. Every pack starts in log-only mode. Eight ordered readiness checks prove that enforcement is observed, not just configured, and zero traffic reports honestly as no data, never as a pass.

Governance Proxy

A stateless proxy service that runs in your own estate and scales horizontally. Existing OpenAI-compatible and Anthropic-compatible clients simply point at the proxy. The proxy holds the actual provider credential, so agents cannot bypass model and guardrail enforcement by calling the provider directly.

Setup Wizards

The Agent Launch and Guardrails Setup wizards take an admin from agent identity through binding, simulation, and readiness review to explicit enforcement activation, without writing policy JSON. Production activation requires typed confirmation and a reason.

Command Center

A permission-filtered Command Center highlights urgent work, recent targets, and recommended setup actions. Domain-scoped navigation, global search, a command palette, and breadcrumbs keep large deployments easy to operate.

Curated RBAC Roles

Twenty persona-aligned system roles, from Platform Admin and Governance Operator to Security Auditor, each grant an exact, documented permission bundle. Identity provider groups map directly to role names, and fine-grained permissions remain the enforcement contract underneath.

Beyond Logging

Logs explain the incident. Guardrails prevent it.

Logs are useful after the fact. They are not enough for AI governance. Syncalytics moves control into the runtime and the workflow itself.

Guardrails inspect

Content is evaluated while the event is happening, before it reaches a model, tool, memory store, or downstream agent.

Policies decide

Every operation gets an explicit decision on whether it should proceed, with a reason code attached.

Approvals capture accountability

High-impact actions wait for a human decision, and the platform records who approved what and why.

Lineage preserves evidence

Traces and lineage keep an audit-ready record for risk, security, legal, and operations review.

Readiness and conformance then show whether each integration is truly enforcing governance, not only configured on paper.

Enterprise Ready

Built for production, not just pilots.

Everything you need to run governed AI operations at enterprise scale, from native SDKs to certified conformance and portable configuration.

Python and Java SDKs

Native SDKs include guardrail evaluation, sanitize-apply helpers, decision and finding reads, metrics, and readiness helpers. Client fail mode is explicit and configurable. Other runtimes integrate through the documented OpenAPI contract.

Conformance certification

Conformance runs execute scenarios through your live integration and certify CORE or FULL coverage only when persisted decision and trace evidence corroborates the report.

Kubernetes readiness

Deploy on Kubernetes with multipod readiness, so governance scales alongside the agent estate it protects.

Archive import and export

Move governed configuration safely between environments, from development through staging to production, without manual reconstruction.

Redacted support bundles

Downloadable diagnostics contain ids, hashes, redacted decision summaries, readiness, and configuration. Never raw prompt, tool, model, RAG, or memory content.

Get Started

Start governing AI agents today.

Get hands-on with the free Community Edition, or see how teams in banking, insurance, manufacturing, government, and beyond put AI Governance to work.

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