PRACYVA
Project Manager - Cybersecurity & AI Experience
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Role Purpose
Lead the end-to-end delivery of AI-enabled cybersecurity capabilities that reduce risk and improve detection, response, and resilience. You’ll coordinate cyber, data, engineering, and risk/control stakeholders to take use cases from concept to production—safely, compliantly, and with measurable outcomes.
Key Responsibilities
Delivery leadership (end-to-end)
- Own delivery plans, milestones, dependencies, and RAID across multiple cyber-AI workstreams.
- Run delivery cadence (Agile/Hybrid), ensuring predictable execution and clear reporting to senior stakeholders.
- Translate cyber priorities into deliverable backlogs and release plans (MVP → scale).
Cyber AI use cases (typical scope)
- Security operations (SOC): alert triage, correlation, enrichment, prioritisation.
- Threat detection: anomaly detection, UEBA-style patterns, phishing/malware classification support.
- Vulnerability management: risk-based prioritisation, remediation insights.
- Identity & access: behavioural signals, privileged access monitoring.
- GenAI for cyber: analyst copilots, investigation summarisation, playbook assistance (with guardrails).
Data, engineering & platform coordination
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Partner with cyber engineering, data science, and platform teams to ensure:
- Data sourcing, quality, lineage, and access approvals.
- Secure model deployment patterns (e.g., isolated environments, secrets management).
- Monitoring for model performance, drift, and operational health.
Risk, controls & responsible AI (critical)
- Ensure alignment with information security, privacy, model risk management, and regulatory expectations.
- Drive documentation and approvals: model cards, threat modelling, DPIAs (where needed), validation evidence, audit trails.
- Implement guardrails for GenAI: prompt controls, data leakage prevention, human-in-the-loop, logging, and incident response.
Stakeholder management & governance
- Establish governance forums (working group/steerco), decision logs, and escalation paths.
- Communicate progress in plain language: outcomes delivered, risk posture, and next steps.
- Coordinate third parties/vendors where applicable and manage delivery outcomes.
Value realisation & operational readiness
- Define success metrics (e.g., reduced MTTD/MTTR, improved precision/recall, analyst time saved, fewer false positives).
- Drive adoption: training, runbooks, support model, and handover to BAU operations.
- Track benefits post-release and iterate based on SOC feedback and performance data.
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Required Skills / Experience
- Proven delivery leadership for complex cyber and/or data/AI initiatives (multi-team, enterprise scale).
- Strong understanding of cybersecurity operations and controls (SOC processes, incident response, detection engineering concepts).
- Working knowledge of AI/ML lifecycle and MLOps/ModelOps concepts (deployment, monitoring, drift, retraining).
- Experience delivering in regulated environments with strong governance and audit requirements.
- Excellent stakeholder management and ability to influence across cyber, tech, and risk functions.
Desirable Skills / Experience
- Experience with SIEM/SOAR ecosystems and security telemetry (e.g., logs, EDR, network, IAM signals).
- Familiarity with threat modelling (e.g., STRIDE), adversarial ML considerations, and secure AI patterns.
- GenAI delivery experience with enterprise guardrails (RAG, red-teaming, prompt injection mitigation).
- Cloud security and data governance exposure.
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