Runtime Defense Agents

Your AI Security Engineering Team. Running Inline.

Defending every attack surface in runtime at AI speed.

Impart's Runtime Defense Agents patches in minutes, investigates findings continuously, reports without a ticket, and tests your defenses before an attacker does. Your AppSec team orchestrates the work instead of doing it.

Meet the Team

A security engineering team that never breaks state.

Security teams don't lose to attackers because they lack tools. They lose because the work outpaces the people.

The Runtime Defense Agents closes that gap by running detection, response, testing, and reporting continuously on the same engine that's already enforcing your traffic.

Patching Agent

Role: Blue team. Rapid patch and test.

The Patching Agent writes and deploys rules against live traffic, validates them on real requests, and pushes them to enforcement in minutes. Zero-days no longer wait for the next sprint. One-click rollback keeps everything safe and fast.

Detection Agent

Role: Threat analysis and investigation.

The Detection Agent triages every finding with full session and rule context, then pivots through related events the same way your analysts do. Novel patterns escalate to a human with the agent’s complete reasoning attached. Known patterns are handled inline automatically.

Reporting Agent

Role: Reporting and compliance.

The Reporting Agent turns live runtime activity into the exact artifacts auditors and stakeholders need. It delivers real-time posture grades, rule effectiveness, and coverage against frameworks like OWASP LLM Top 10 and OWASP Agentic. All generated from live data, never assembled the night before a review.

Testing Agent

Role: Red team. Continuous AI pen testing.

The Testing Agent continuously probes your entire attack surface the way a real attacker would, then hands findings directly to the Patching Agent. Your red and blue teams now share the same runtime. The window between exploit discovery and enforcement gets measured in minutes.

One runtime engine. Every model interaction.

Position

Inline across APIs, services, and agent-driven workflows

Context

Maintains sequence and session state across the full interaction

Timing

Enforces synchronously — no async gap between detection and block

Control Plane

Unified detection and enforcement in one system

How it Works

One team, one loop, inline.

The agents run continuously as a loop. Findings become understanding. Understanding becomes response. Response becomes improvement. The runtime that protected you yesterday is sharper today, without a vendor update or a manual rule change.

Step 1

Discover The inline inspector tags every request against full detection coverage, with cross-surface session context. Behavioral history starts building immediately across every endpoint, identity, and tenant.

Step 2

Detect The Detection Agent classifies findings against your rules, your tags, and your traffic baseline. Multi-turn investigation pivots through related events using the same toolkit your team uses.

Step 3

Protect Known patterns trigger auto-response inline. Novel patterns route to a human with the agent's full reasoning attached. The Patching Agent proposes the rule that closes the gap. The Testing Agent validates them. Enforcement fires before the action lands.

Step 4

Improve False positives train the system. Coverage gaps propose new rules. The Reporting Agent updates posture grades inline. The next attack hits a sharper system than the last one. The defenses you ship tomorrow are tested against the attacks that haven't been published yet.

FAQ

Why do AI-native attacks require AI-native defenses?

AI agents probe hundreds of endpoints in parallel, chain valid-looking requests into multi-step exploits, and complete attacks faster than a human analyst can reach the alert. Static rules and periodic reviews can't keep pace with traffic that adapts in real time. AI-native defenses run inline at the application layer, evaluate the full session instead of a single request, and update enforcement continuously from observed behavior. The defenses operate on the same timescale as the attacks, on the same data plane that handled them.

What is an AI security engineering team, and how is it different from a SOAR or AI copilot?

A SOAR runs static playbooks against alerts. A copilot suggests next steps to an analyst. The Agentic Runtime Team is different because the agents operate inline on the same data plane that's enforcing your traffic, not as a layer sitting on top of yesterday's logs. The Detection Agent investigates with the same toolkit your engineers use. The Patching Agent writes and deploys rules against live traffic. The Testing Agent probes the runtime adversarially. The Reporting Agent assembles compliance evidence continuously. The team isn't recommending work to a human. It's doing the work, with the human supervising outcomes.

How fast can the Patching Agent close a zero-day in production?

Minutes. The Patching Agent writes rules against live traffic, validates them against real requests, and pushes them to enforcement without a deploy or a code change. The window between disclosure and protection collapses from the length of a release cycle to the length of a coffee break. Rollback is one click. Most teams run new rules in observe mode first, watch the decisions against real traffic, then promote to enforcement once they're confident.

How does Impart help with OWASP LLM Top 10, OWASP Agentic, MITRE ATLAS, and NIST AI compliance?

The Reporting Agent maps observed runtime activity to the controls these frameworks define. Prompt injection, sensitive data leakage, excessive agency, unauthorized tool use, supply chain risk, and the action-layer items in OWASP Agentic all show up as enforcement decisions on Impart's runtime, with full session context attached. Posture grades, rule effectiveness scores, and coverage gaps generate inline. Audit artifacts come from live data, not from a spreadsheet built the week of the review.