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.
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 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.
Running in production. Enforcing in real time.
"The Impart team is really innovating in the API security space. Really smart use of LLMs in their product that help security teams especially with firewall rules, which are a huge problem."
"API security is now a critical aspect of every application security program. Every CISO needs to have an integrated solution that can comprehensively protect their APIs across their entire lifecycle."
"Great product. Great team. Makes application security so much easier and installs in minutes across both legacy and modern tech stacks."
"When we think about examples of customer love in cybersecurity, some of the most loved companies in security includes Impart Security."
"Hands down one of the best API security products on the market and the most compelling solution for serverless. Integrates with no architecture impact, and great team to work with."
"Examples like Thinkst Canary, Duo Security, Tines, Chainguard, Material, Impart, Panther, Anvilogic, and LimaCharlie show that it is possible to be pragmatic (and successful!) as a business and loved at the same time."
"The team is building something truly top notch in WAF, API Security, and LLM Protection."
All
runtime decisions
"Nothing drives me more than getting to work with highly motivated and super intelligent people. I am happy to be here and looking forward to the long road ahead!"
"Impart is my pick to lead the next wave in application security tooling by leveraging usage (and other) context for decisions and making it visible to both security teams and developers. This unifies two themes in security today: Shift Left and Protect Right."
"I have a sophisticated app sec team, and they regularly complain about how limiting form-based rule builders are. They will be pumped to hear about the ability to build more sophisticated rules via code. Same with dynamic runtime lists. The LLM-powered rule explainer is also pretty cool. It is gen AI that is actually useful, as opposed to framing in another gen AI chatbot and calling it a day."
"Impart offered Crossbeam a single, unified solution for Web application, API security, and LLM protection.The team has provided exceptional support and is a true partner for us."
"Impart has everything you'd want in an API security platform, and there's little reason to look elsewhere - they provide discovery, testing, and protection—all in a single platform. Impart’s combination of accurate discovery with anomaly detection made them stand out in a crowded space filled with other great tools."
"Impart saved the day during a security incident when our WAF and our SIEM failed to detect and mitigate an ongoing API attack. Impart effortlessly detected and stopped the attack for us, with great support from the team."
"We've dramatically reduced our cycle time for adapting to new threats—we can now match the velocity of attackers instead of always playing catch-up. Impart has made our entire security operation more surgical and effective."
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100%
FAQ
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.
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.
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.
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.