Application SecurityAI Security
Impart Security Platform
Inline runtime enforcement engine that blocks API, web, LLM, and MCP attacks across full request sequences before they reach production backends.
Impart Security Platform Overview
The Impart Security Platform is a runtime application protection platform that inspects web, API, large language model (LLM), and Model Context Protocol (MCP) traffic through a single inline enforcement engine. Rather than alerting after an attack lands, it evaluates every request in the path of live traffic and decides whether to allow, block, or modify it before the request reaches the backend. Detection logic compiles to WebAssembly, so enforcement runs at near-native speed regardless of how many rules are active.
Each request is evaluated against a shared per-entity session store that retains about 30 days of behavioral context, correlating reconnaissance, probing, and exfiltration across sessions, identities, and tokens as a single sequence rather than isolated events. Security teams write code-based rules in AssemblyScript that are regression-tested against production traffic before they enforce, and the engine proposes new rules from observed attack behavior and deploys them as virtual patches within minutes. Patching, detection, testing, and reporting agents run on the same engine and map activity to frameworks including OWASP LLM Top 10, OWASP Agentic, MITRE ATLAS, and NIST AI.
Impart Security holds SOC 2 Type II certification and was founded by veterans of Signal Sciences, the runtime application security company acquired by Fastly. Crossbeam uses the platform to consolidate web application, API, and LLM protection in one engine. The company raised a $12 million Series A led by Madrona Ventures, with CRV and 8-Bit Capital participating, and sells through the AWS Marketplace. It targets security and application security teams defending production systems.
Key Capabilities
mapped to solution categoriesDetects and blocks malicious API behavior at runtime using anomaly and behavioral analysis trained on attack patterns.
Detects and rate-limits automated abuse, credential stuffing, scraping, and misuse of sensitive business flows.
Tests APIs for vulnerabilities using static and dynamic analysis, often integrated into the development pipeline before release.
Detects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.
Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.
Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.
Signature- and rule-based detection and blocking of common web attacks such as those in the OWASP Top 10.
Rapid policy-based mitigation of newly disclosed application vulnerabilities without changing application code.
Machine learning and behavioral analysis to detect anomalous traffic and reduce false positives beyond static rules.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on June 27, 2026