Built to Block AI Attacks in Production
One enforcement engine across every surface, engineered so teams never have to choose between blocking and performance.
How Impart blocks at production speed.
Impart's enforcement engine compiles all detection logic to WebAssembly. That is what makes production-grade blocking possible without the tradeoffs that keep other tools in monitor mode.
Millisecond Execution
Every rule, built-in and custom, compiles to WASM and runs at near-native speed. Processing time does not scale with rule count.
Sandboxed
Rules run in an isolated, memory-safe sandbox. A custom rule cannot crash the engine, corrupt state, or interfere with other rules.
Portable
Same WASM-compiled enforcement across EKS, GKE, AKS, self-managed. What you validate in staging enforces the same way in production.
Custom Rules at Platform Speed
AssemblyScript rules compile to WASM and run at the same speed as built-in detection. No performance penalty for specificity.
No Sidecar, No Proxy Chain
Inline at the application layer. Deployed as a Kubernetes ingress controller. No extra hops.
Fits where you already run.
Drop Impart into your existing stack — API server, gateway, Kubernetes, or PaaS. No architecture changes required.
Every rule meets the TRACE standard.
Every rule in Impart, built-in and custom, meets five requirements before it enforces on live traffic. This is why blocking stays on.
Tested.
Regression tested against production traffic before deployment. A rule that blocks legitimate requests never reaches production.
Reversible.
Instant rollback in seconds via immutable rule versions and automated health checks. No downtime. No deploy cycle. No ticket.
Auditable.
Full revision control. Who changed what, when, and why. SOC 2 and SOX ready.
Code-based.
Deterministic, readable AssemblyScript. Not opaque model outputs. Tracked in version control. Reviewable by any engineer.
Explainable
Under pressure, during an incident, in front of auditors, engineers reconstruct every blocking decision line by line.
Measured in production
95%
90%
1.5M+
Every attack strengthens the block.
Impart does not wait for a human to write the next rule.
Step 1
An attacker probes your application.
Step 2
Impart detects the pattern and logs signals across the session.
Step 3
AI generates a new rule in AssemblyScript.
Step 4
The rule goes through the full TRACE process.
Step 5
It compiles to WASM and deploys inline.
Step 6
The next attempt is blocked on the first request. Automatically.
FAQ
Shift left moves security earlier in the development lifecycle, focusing on finding vulnerabilities before code ships. Runtime security operates after deployment, inline in the path of live traffic, detecting and blocking threats as they happen. A runtime protection platform is the layer that catches what shift left cannot: threats that have no pre-deployment signature, behave like legitimate traffic, and complete in milliseconds.
AI agents pursue goals across sessions, probe multiple surfaces simultaneously, and adapt continuously. Stopping them requires inline enforcement at the origin, behavioral detection that models intent across sessions rather than matching signatures, and a shared data layer across every surface so a single agent cannot get a clean slate by switching attack vectors. That is what a runtime protection platform is built for.
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.
Runtime enforcement is the ability to detect and block a threat at the moment the request is made, inline in the path of live traffic, before it reaches your application. It is distinct from detection-only tools that observe traffic and alert after the fact, and from shift-left tools that look for vulnerabilities before deployment.
Stop AI attacks before they finish.