SPLX AI Security Platform
End-to-end AI security platform for red teaming, runtime protection, and governance of LLM systems.
Vendor Information
SPLX AI Security Platform Overview
SPLX is an end-to-end AI security platform that protects LLM-powered applications and multi-agent systems across the entire AI lifecycle from development through deployment to runtime operation. Unlike traditional security tools that rely on manual testing and point-in-time assessments, SPLX provides continuous automated red teaming, real-time threat detection, and AI asset discovery through seamless integration into CI/CD pipelines and production environments.
The platform combines automated red teaming with the industry's most comprehensive AI attack database containing 5,000+ attack simulations, AI Runtime Protection with real-time input/output guardrails, AI Asset Management for complete visibility into models and agentic workflows, and automated compliance mapping to NIST AI RMF, OWASP LLM Top 10, and EU AI Act. SPLX's continuous monitoring approach detects prompt injections, jailbreaks, data leakage, and off-topic responses with near-zero latency while providing dynamic prompt hardening that reduces attack surface by up to 95%.
Founded in 2023 and headquartered in Dover, Delaware, SPLX raised $9M in funding led by LAUNCHub Ventures with participation from Rain Capital, Inovo, Runtime Ventures, DNV Ventures, and South Central Ventures before being acquired by Zscaler in November 2025. The platform holds ISO 27001 and SOC 2 certifications, serves Fortune 500 enterprises, and processes over 20 AI risk categories across text, images, and voice modalities to secure enterprise AI deployments at scale.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Produces a structured inventory of AI components, training data provenance, model versions, and dependencies in SPDX AI extension or CycloneDX ML profile format.
Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.
Maps what data (including PII, PHI, and IP) flows into and out of AI models and APIs, identifying unintended exposure of sensitive data to external LLM services.
Maps the AI system inventory and associated controls to EU AI Act risk classification requirements and ISO 42001 AI management system controls.
Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.
Detects adversarial inputs designed to override AI system instructions, extract training data, or manipulate model outputs. Detection approaches range from pattern matching to secondary model evaluation.
Identifies AI systems processing sensitive or regulated data without appropriate controls: unencrypted PII in model inputs, PHI flowing to external APIs, IP in fine-tuning datasets.
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.
Enforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.
Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.
Verifies the cryptographic integrity and provenance of AI model artifacts to detect tampering, substitution, or unauthorized modification before deployment.
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