UnifAI Introduces Runtime Defense for AI Agents.

UnifAI Guardrail Server: Runtime Visibility and Control for AI Agents

As organizations deploy AI agents across increasingly complex environments, they need visibility and control over how those agents interact with models, tools, data, other agents, and users.

The UnifAI Guardrail Server, integrated with the UnifAI Policy Orchestrator, provides runtime visibility and control over agent actions. Security, compliance, and governance policies are evaluated in real time as agents execute, enabling organizations to observe interactions and enforce approved controls throughout runtime.

At a Glance

The UnifAI Guardrail Server helps organizations:

Enforce compliance. Apply controls aligned with 18+ global AI acts and standards, enterprise policies, and operating context.
Protect against AI threats. Detect and block AI-specific attacks using the AI Kill Chain and 55 linked AI attack techniques.
Control AI interactions. Apply policy controls to model interactions, prompts and responses, MCP communications, tool calls, agent-to-agent actions, agent-to-MCP interactions, agent-to-LLM interactions, and agent-to-human controls.

Autonomous Intercepts Bring Policy Enforcement into the Agent Workflow

UnifAI scans agentic code as AI tools and humans write it throughout development. IDEs, source control management (SCM), and pull requests are scanned, and policy enforcement points—called Autonomous Intercepts—are automatically identified and inserted, with built-in human approval workflows.

When agents run, these intercepts invoke the Guardrail Server, providing visibility and control points throughout execution. Intercepts are evaluated in real time against centrally defined security and compliance policies active at the time of execution.

This means organizations can update policies and have them take effect immediately without changing the agents themselves.

Developers can focus on building better agents without independently implementing every security and compliance requirement during development and deployment. GRC, Legal, Security, and AI teams can define additional intercept policies for enterprise best practices and vertical-specific requirements.

Build an Agent Once. Govern It Wherever It Operates.

UnifAI dynamically applies security, compliance, and governance policies across jurisdictions, enterprise policies, users, agents, data, and execution contexts—without requiring separate agent implementations.

Based on the applicable jurisdiction, enterprise policy, user, agent, data classification, or execution context, UnifAI can allow or block an action, require human approval, redact or mask sensitive information, restrict tool or model usage, or apply other approved controls before execution continues.

This gives organizations consistent runtime security, compliance, and governance controls across SaaS, on-premises, and hybrid environments while supporting regional regulations, industry standards, and enterprise-specific AI policies.

How the UnifAI Guardrail Server Works

The Guardrail Server places runtime checks throughout agent workflows where agents interact with models, tools, documents, and other agents.

As shown in the Guardrail Server workflow, controls can be applied throughout the agentic workflow—from inbound prompts and documents through LLM, MCP, tool, and agent interactions to the outbound response.

At these control points, UnifAI can block malicious input, protect sensitive data, enforce approved model use, screen model output, inspect tool calls, validate tool results, control agent handoffs, and review returned results and outbound responses.

The Guardrail Server connects these runtime interactions to the centrally managed policies and rules in the UnifAI Policy Orchestrator.

Key Capabilities

Discovery and Runtime Visibility

Discovers agents, sub-agents, LLMs, MCP servers, tools, execution paths, and dependencies. UnifAI builds an Agent Blueprint—an agent topology and dependency graph—that provides visibility into agent interactions and makes complex agentic applications easier to understand, trace, and govern.

Runtime Visibility & Auditability

Provides visibility into agent activity and runtime interactions across models, MCP servers, tools, data, other agents, and users. Combined with policy evaluation and enforcement visibility, this helps security, compliance, and engineering teams investigate agent behavior and support governance requirements.

Runtime Detection and Policy Enforcement

Detects agent behavior and actions that violate approved security, compliance, or operational policies, including unauthorized model usage, unsafe or unauthorized tool calls, prohibited execution paths, prompt-injection attempts, sensitive-data leakage, and other non-compliant runtime activity.

Through the Guardrail Server, policies can be enforced in real time by allowing or blocking actions, requiring human approval, redacting or masking sensitive information, restricting tool access, or applying other approved remediation and containment controls.

Global and Jurisdiction-Aware Policy Enforcement

Enables organizations to build agents once and operate them across multiple countries and enterprise environments while applying appropriate policies at runtime. Controls can vary by jurisdiction, business unit, customer, user, agent, data type, or deployment environment.

This allows compliance controls to evolve independently of the agent implementation, reducing the need to modify, fork, or rebuild agents for each operating environment.

Automated Instrumentation

Automatically identifies and inserts policy enforcement points across model calls, MCP interactions, and tool invocations. Intercepts invoke the Guardrail Server at runtime and are evaluated against centrally managed policies active at execution.

Centralized Policy Management

Provides a centralized policy layer to define and manage security, compliance, and AI governance controls across distributed agents. Policies can be updated centrally and enforced across deployed agents without requiring individual agent teams to implement each regulatory or enterprise requirement independently.

Flexible Deployment

Supports deployment across SaaS, public cloud, private cloud, hybrid, on-premises, and air-gapped environments while maintaining centralized AI governance, policy enforcement, visibility, and reporting.

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