Phil Rushton
Cyber Security Lead @Bionic
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WORK HISTORY
Cyber Security Lead @Bionic
London, GB
Reporting to CISO- Define, managed and maintained the Secure Software Development Lifecycle (SDLC)(Snyk, Wiz, SCM)- Technical facing cross functional management to deliver expertise and capabilities across software engineering, architecture, data and insights, Salesforce, IT, service desk, infrastructure and senior stakeholders- Using Generative AI for automations and defining enterprise level controls and guardrails- Security Champions Network and Advocacy- Security Architecture, Principles Process and systems management- Secure by Design- Cloud Security, AWS, WAF, SCPs, RCPs- Governance Risk Compliance, Enterprise Risk Management, NIST CSF 2.0, OWASP SAMM- Data Governance Framework, ROPA, GDPR, Data Security - Security Operations, SIEM, Firewalls, Threat Intelligence, Microsoft Purview - DLP, Microsoft Enterprise tech stack - Incident Response, Business Continuity, Leading Disaster Recovery Exercises- DevOps, CI/CD, GitHub Security- Vulnerability Management (Snyk and Wiz Code to Cloud, Wiz Sensor), serverless, containerisation (ECR, ECS)
EDUCATION
University of Liverpool
Master of Engineering - MEng (hons), Mechanical Engineering
King's College London
Master of Science - MSc, Advanced Cyber Security
ABOUT PHIL RUSHTON
I am currently a Cyber Security Lead specialising in cloud and software security for private-equity backed scale-ups with experience in high-assurance (defence and government) environments.I am a chartered Mechanical Engineer (CEng) with a background in nuclear and defence, bringing first-principles engineering, systems thinking, and disciplined risk management into modern cyber security.I design and lead technical security programmes that enable security at scale: secure cloud platforms, embedded SDLC security, proportionate and pragmatic governance, and clear translation of technical risk for executives and investors.MEng, MSc Advanced Cyber Security, CISSP.I am currently studying a Level 7 (Masters) apprenticeship in AI and Data Engineering, with a focus on the secure adoption of AI and machine learning systems to automate and increase efficiency.
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