Impart Security Platform

Provides real-time inbound and outbound monitoring and input/output guardrails for AI-powered applications to prevent unauthorized data exposure and unsafe model responses.

Controls request volume per user or client within defined time intervals.

Rapid policy-based mitigation of newly disclosed application vulnerabilities without changing application code.

Signature- and rule-based detection and blocking of common web attacks such as those in the OWASP Top 10.

Machine learning and behavioral analysis to detect anomalous traffic and reduce false positives beyond static rules.

Detection and mitigation of malicious automated traffic and advanced, evasive bots.

Detects broken object-level and function-level authorization, where a caller can reach data or operations belonging to another user or role.

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.

Continuously discovers and inventories all APIs across the environment, including shadow and zombie APIs that are not tracked in the official catalog.

Detects and blocks malicious API behavior at runtime using anomaly and behavioral analysis trained on attack patterns.

Identifies APIs that transmit or return sensitive data such as personal information, credentials, or tokens, so exposure can be flagged and controlled.

Validates live API traffic against the documented OpenAPI or schema definition to catch undocumented endpoints, unexpected parameters, and drift.

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.

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.

Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.