About

CRAIG ALLAN-MCWILLIAMS

I am an independent cybersecurity consultant and Chartered Enterprise Security Architect. I work globally with enterprises and government organisations on the problems that resist a framework and a project plan: security architecture, agent identity and authorisation, cryptographic provenance, cloud and delivery-pipeline security, threat modelling, and the governance and assurance work that makes all of it defensible.

My current development focus is the secure adoption of generative and agentic AI for critical defence missions and offensive red-agent deployments. In practice that means helping organisations establish enforceable authority for AI agents: least-privilege access, identity, policy controls, runtime boundaries, auditability, and evidence for intelligence systems.

Raucle

The research side of my practice is raucle, the open-source division of EPIC28. Raucle builds AI security tools in the open. Its first project is a trust layer for AI agents, designed to make agent authority enforceable and agent activity independently verifiable: signed receipts for every action an agent takes, agent identity, portable trust, cross-organisational handshakes, revocation, and compliance evidence. The source is at github.com/epic28-ltd/raucle, released under Apache-2.0.

The first Raucle tool was built to understand what agents actually do in offensive red-team engagements. That work held up, and the tooling has since been opened for regulated and safety-critical environments: financial services, payments, healthcare, retail, manufacturing, defence, critical infrastructure, and government.

It has been through nine rounds of adversarial security testing, with security assumptions and limitations documented and published. My contribution to Microsoft’s Agent Governance Toolkit was accepted upstream. I also contribute to open-source projects across the agentic-AI enabler stack, including OpenClaw, NemoClaw, and nono.

The questions I work on are not theoretical

  • How should trust boundaries work for autonomous agents?
  • How do you audit agent decisions in real time?
  • How do you stop an AI system becoming a lateral-movement vector into critical infrastructure?
  • How does an organisation prove that an agent acted within its authority and policy?
  • What security controls do you need when agents can call tools, access data, and trigger workflows on their own?

Organisations are already deploying agents into systems that hold sensitive data, privileged identities, and operational authority. These questions are not future concerns. They are current ones.

Experience

I have spent over 25 years as a security engineer, architect, consultant, and adviser across banking and capital markets, central government and defence, manufacturing, retail, telecoms, and software security. That includes CI/CD pipeline security, AWS security architecture at scale, security monitoring and forensics, enterprise risk management, application and cloud security, and security programmes built from nothing up to operational.

I hold SABSA Chartered Security Architect, CISSP, AWS Certified Security Specialty, CCSK, and MITRE ATT&CK credentials. My approach treats security as a system property: something designed into architecture, identity, software delivery, operations, governance, and organisational decision-making. Not a control layer added at the end.

I bring both sides of the problem. The technical depth to architect and engineer secure infrastructure, and the commercial fluency to explain risk, trade-offs, and investment decisions to boards and executive teams. Security architecture that an executive cannot understand is architecture that does not get funded.

Working together

EPIC28 takes engagements across governance, assurance, intelligence, architecture, threat modelling and control implementation. You can see the practice at epic28.com, or reach me through LinkedIn.

I write about cybersecurity and AI security here at All Things on Security, covering agentic AI, AI governance as architecture, threat modelling, fail-open vulnerabilities, quantum security, and securing AI across the software-delivery lifecycle.

Areas of focus

  • Agentic AI and AI-agent security
  • Agent identity, authority, and authorisation
  • Cryptographic provenance and accountability
  • Security architecture and SABSA
  • AWS and cloud security
  • CI/CD and DevSecOps security
  • Threat modelling and adversarial testing
  • AI governance, assurance, and audit evidence
  • Enterprise cyber risk and security transformation
  • Open-source security engineering