As enterprises deploy LLMs, copilots, RAG pipelines, and autonomous agents, CoreLayer AI Security is the only platform that secures every phase — from build time to end-user interaction — in a single, unified intelligence suite. At the Build phase, we scan prompts and templates before a single line ships. At Test, we simulate 2,000+ adversarial attacks. At Validate, we secure your RAG pipelines and guardrails. At Runtime, we enforce live inference protection. And at the End User layer, we mask sensitive data before it ever reaches an LLM.
Each module is powerful standalone. Together, they form a cross-phase intelligence loop that gets smarter with every threat.
Deep instruction hierarchy analysis, role override detection, context boundary evaluation.
Covers prompt injection, instruction override, unsafe roles, missing refusal logic, over-permissive tools.
Every finding mapped to OWASP LLM Top 10 with severity, exploit simulation, and remediation guidance.
Zero cloud dependency. Zero prompt content leaves the machine. Run from your terminal directly.
15 categories: role confusion, instruction negation, policy bypass, output escape, multi-turn coercion.
Reconnaissance → Attack (adaptive multi-turn) → Verification. Deterministic, reproducible results.
Native GitHub Actions integration. Build fails if attack success rate exceeds configurable threshold.
Attack prompts, model responses, success rate per category, OWASP mapping, remediation priority.
Validates vector DB config, embedding scope, access controls, retrieval parameters, cross-tenant leakage.
Pre-deployment gate validating system prompt hardening, tool permission boundaries, output validation rules.
0–100 score with missing-guardrail findings, hardened prompt suggestions, and deployment readiness certification.
Identifies poisoned embeddings, unsafe chunking strategies, over-permissive metadata access.
Controls WHAT the model sees. Identity-aware inference boundaries, tenant isolation, YAML policy-as-code.
Detects HOW the model behaves. Per-model behavioral fingerprints catch zero-day jailbreaks.
Limits WHAT the model can do. Hard-limits on tool chaining depth, execution ceilings, resource consumption.
LBF detects jailbreak → LCAC auto-tightens → CBE lowers ceilings. Runtime feeds back to Build rules.
Detects and masks API keys, passwords, secrets, email, phone, Aadhaar, PAN, credit cards, IFSC, UPI IDs.
Local-first architecture. Zero data collected, stored, or transmitted. Enterprise-grade privacy by design.
Available as SDK (Python/Node/Go), browser extension, CLI tool, and API proxy mode.
Complete audit log of all masked interactions, compliance-ready reporting, enterprise DLP integration.
Each phase feeds intelligence to the others. A vulnerability found during Build generates a Test attack case. A runtime anomaly feeds back into Build scanner rules.
From asset discovery to active enforcement — CoreLayer instruments your AI security lifecycle in three structured phases.
CoreLayer is cloud-agnostic and architecture-agnostic. Deploy in private, public, or hybrid environments with zero compromise on security posture.
From healthcare data privacy to financial fraud prevention — CoreLayer's platform adapts to the compliance and risk profile of your industry.