Cybersecurity Analyst
Posted 12 Oct 2026
Advertised as “Senior Cybersecurity Lead Analyst”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8–10 yrs
- Management level
- People management
No salary listed for this role.
CertificationsCISSP, CISM, CEH
Field of studyComputer Science, Cybersecurity, Engineering, Information Technology (or related)
Study levelBachelor's or higher
Hands-on Senior Cybersecurity Lead to drive application, product, and offensive security across our cloud platforms. You will lead penetration testing, application security, and vulnerability management; embed Secure SDLC/DevSecOps with engineering; secure and govern AI-enabled capabilities; and communicate security risk clearly to technical and business stakeholders. This role sets security practices, mentors others, and raises the security posture of Platform Services.
Key Responsibilities
- Lead application & product security across internal and cloud-based apps; drive Secure SDLC / DevSecOps with engineering.
- Own penetration testing for web, API, cloud, and infrastructure – scope, test, report, track remediation, retest.
- Run vulnerability management with risk-based prioritisation (exploitability, exposure, impact, asset criticality) and remediation to closure.
- Provide cloud security guidance for AWS and/or Azure – review IAM, network, logging, encryption, and CI/CD security.
- Integrate security testing and automation into CI/CD; improve secure-coding practices and tooling.
- Secure & govern AI: control and monitor AI/LLM and AI-assisted development risk across the platform (see AI Security & Governance below).
- Review designs to catch risk early (architecture/design reviews, threat modelling).
- Lead & collaborate: mentor security staff, act as SME, and partner with Engineering, DevOps, Architecture, and Product.
- Governance: support ISO 27001 / SOC 2 controls, audits and evidence, and security metrics/reporting.
AI Security & Governance
- Establish how AI-related risk is identified, secured, and continuously monitored across AI/LLM-enabled features and AI-assisted development in Platform Services.
- Define and enforce security controls and governance for AI/LLM-enabled applications and AI-assisted development tools.
- Identify and mitigate AI-specific risks: sensitive-data and source-code exposure, prompt injection, insecure or hallucinated output, excessive tool/agent permissions, model and supply-chain risk, and IP/licensing exposure.
- Apply least-privilege access for AI tools and agents, tool allowlists, retrieval/data-loss controls, and input/output validation.
- Continuously monitor and log AI usage, prompts, and agent actions; set alerting, anomaly detection, and human-oversight/escalation for AI-driven workflows.