CoreLayer Security
About UsLayers Of Trust For AI Workflows
CoreLayer Security combines offensive expertise with intelligent platforms to uncover hidden security gaps in AI and complex systems, enabling organisations to build resilience and operate securely in an ever-evolving threat landscape.
About CoreLayer AI Security
AI is being embedded into enterprise operations faster than security can follow. System prompts go unreviewed. Models are tested functionally, never adversarially. Runtime behaviour is assumed safe. The result is an expanding attack surface that traditional security tools were never designed to address.
CoreLayer AI Security was founded to close that gap - not with another point solution, but with a full-lifecycle platform purpose-built for how AI systems actually work.
Our founders bring together enterprise cybersecurity experience, deep adversarial AI research, and a track record of critical vulnerability disclosures recognised by organisations including Google, the United Nations, and the World Health Organization. We have seen how security debt accumulates when it is added after the fact. CoreLayer was built from day one to instrument security across every phase of AI deployment - from the first system prompt to runtime inference to end-user interaction.
Our Mission
To make AI security continuous, adaptive, and lifecycle-aware - so enterprises can deploy AI with confidence, not compromise.
Challenge
AI is scaling. Security is not.
Every AI system passes through five critical phases. Each introduces unique, exploitable risks and today's fragmented security tools address only fragments of this lifecycle.
Five phases. Unlimited attack surfaces.
Traditional security tools were built for static software. AI systems are dynamic reasoning engines. The threat operates inside the inference loop.
Build
Prompt Injection
System prompts written without static security analysis. Attackers exploit injection surfaces before deployment.
Test
Jailbreak Blindness
Models tested functionally, not adversarially. Attack surfaces remain invisible until production.
Engagement methodology
Threat Modeling
Define attack surface, adversary personas, and deployment-specific risk scenarios tailored to your environment.
Reconnaissance
Enumerate model capabilities, system prompts, API surfaces, tool integrations, and data access boundaries.
Exploitation
Execute adversarial attack chains across prompt injection, extraction, and agentic abuse vectors with full evidence capture.
Post-Exploitation
Assess lateral movement potential, data exposure scope, and downstream system impact from a compromised model state.
Reporting
Severity-rated findings, evidence packages, and remediation guidance mapped to recognised control frameworks audit-ready.
Built for enterprise
CoreLayer operates under strict NDA and data handling agreements. All engagements are structured for regulated industries financial services, healthcare, defence, critical infrastructure.
Air-gapped and on-premises engagement options are available for classified or restricted deployments. Our practitioner team holds OSCP, CREST, and ML-security certifications with background-checked clearance history.
Retainer arrangements provide continuous red-team coverage as your AI systems evolve. Every engagement produces documentation suitable for board review, regulatory submission, and cyber insurance audit.