From the first system prompt to runtime inference to end-user interaction — CoreLayer SecureAI instruments security at every phase of AI deployment. Each module is powerful standalone. Together, they form a cross-phase intelligence loop that gets smarter with every threat.
LLM Risk Scanner — Static analysis of prompts, templates & tool configurations before a single line ships to production.
Most AI security vulnerabilities originate at the Build phase — in the system prompts, tool configurations, and instruction templates that define how a model behaves. Radar is a static analysis engine that identifies these vulnerabilities before deployment, when fixing them costs nothing and ignoring them costs everything. It runs entirely locally, meaning zero prompt content ever leaves the machine.
cl-ai scan ./prompts directly from your terminal or embed in GitHub Actions as a pre-deployment gate. Build fails if any Critical findings remain unresolved. Security becomes part of the engineering contract.CoreLayer Radar runs entirely locally. No cloud dependency, no data upload, no risk.
Adversarial Simulation Engine — 2,000+ attack payloads across 15 categories with deterministic, reproducible results and CI/CD pipeline enforcement.
Functional testing tells you if an AI system works. Adversarial testing tells you if it can be broken. Striker simulates the full spectrum of real-world attacks against your LLM — from prompt injection and jailbreaks to multi-turn coercion sequences and data exfiltration attempts. Every test is reproducible, every result is OWASP-mapped, and every attack failure becomes a CI/CD gate.
Striker simulates 2,000+ attack payloads against your live model. Find out before your adversaries do.
RAG Security Analyzer + Guardrail Checker — Validate your entire AI deployment security before going live, with a scored certification report.
The Validate phase sits between development and production — it's your last structured opportunity to find security gaps before real users interact with your AI system. Vault performs two complementary assessments: a deep RAG pipeline security scan covering vector databases, retrieval parameters, and embedding configurations; and a guardrail checker that validates every safety layer your system relies on.
Vault certifies your AI system is production-ready — with a scored report your CISO and board can trust.
Unified Runtime Defense — LCAC (Prevent) + LBF (Detect) + CBE (Enforce) operating as one defense-in-depth layer at inference time.
Once an AI system is live, the threat surface shifts to the inference loop — where real prompts, real users, and real tool executions intersect. Shield instruments the inference graph directly with three complementary engines that operate at sub-10ms latency: LCAC controls what the model sees, LBF detects how it behaves, and CBE limits what it can do. Together they form an adaptive runtime defense that gets smarter as the system is attacked.
Shield deploys in hours, not weeks. Sub-10ms latency. No architecture rewrite required.
Local-first sensitive data masking — intercepts PII, credentials, and secrets before any prompt reaches an LLM, with zero data collection.
Every enterprise employee who interacts with an LLM is a potential data leakage vector — pasting API keys, financial identifiers, patient records, or internal credentials into external AI systems without thinking. SecureAgent operates at the user layer, intercepting sensitive data before it leaves the device. No data is ever collected, stored, or transmitted. It runs entirely locally — privacy by architecture, not just by policy.
SecureAgent is available for enterprise deployment. Zero data collection, maximum protection.