Offensive Security

Job Summary We are seeking a highly experienced, hands‑on Senior Cybersecurity Architect/Engineer specialising in Offensive Security, Red Team operations, AI Security, and Enterprise Security Architecture. The successful candidate will combine strong offensive security expertise with the ability to design, implement, and validate enterprise‑wide security controls across cloud‑native environments, applications, infrastructure, and AI‑enabled platforms. Responsibilities 2. DevSecOps & Secure Engineering 3. Cloud & Enterprise Security Architecture 4. Security Operations, Threat Detection & Incident Response 5. AI Security Architecture & Implementation 6. Data Security, Privacy & Governance 7. Security Risk, Compliance & Governance Requirements
- Offensive Security, Red Team & Purple Team Operations
- Lead Red Team engagements, including planning, scoping, execution, resource allocation, quality assurance, and reporting.
- Manage internal Red Team resources and external penetration testing and vulnerability assessment (VAPT) providers.
- Execute adversarial simulations covering initial access, privilege escalation, lateral movement, identity compromise, and Active Directory attack paths.
- Conduct penetration testing across web applications, infrastructure, networks, and enterprise environments.
- Lead Purple Team exercises to validate detection and response capabilities and identify gaps in existing security controls.
- Translate offensive security findings into actionable remediation plans and improvements to detection engineering.
- Develop or adapt attack scripts, automation, and custom tooling to simulate advanced adversarial scenarios.
- Integrate security testing into CI/CD pipelines, including Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), Software Composition Analysis (SCA), and Infrastructure as Code (IaC) scanning.
- Implement automated security gates, policy-as-code, and security validation workflows.
- Establish secure development practices across containerised environments, including Docker, Kubernetes, and artifact registries.
- Collaborate with software engineering and platform teams to embed security controls throughout the software development lifecycle.
- Automate security checks, compliance validation, and remediation processes wherever possible.
- Design and implement security architecture and guardrails across cloud, on-premises, and hybrid environments.
- Define controls for identity and access management, privileged access, network segmentation, encryption, secrets management, and workload protection.
- Establish secure configuration baselines and integrate security requirements into cloud adoption and infrastructure deployment.
- Assess cloud environments against recognised security frameworks and benchmarks, prioritising risks and coordinating remediation.
- Design and validate disaster recovery, high availability, and resilience strategies across infrastructure, identity, networks, and applications.
- Conduct recovery and failover testing to validate security controls and identify weaknesses in backup, recovery, and resilience mechanisms.
- Improve security monitoring and threat detection capabilities across cloud and on‑premises environments.
- Develop and optimise SIEM detection rules, log ingestion, alert triage, and threat‑hunting processes.
- Lead incident response activities, including investigation, containment, eradication, recovery, and post‑incident reviews.
- Integrate threat intelligence and adversary techniques into detection engineering and proactive threat hunting.
- Work closely with Red and Blue Teams to improve detection coverage, response effectiveness, and overall security maturity.
- Design and implement security controls across the AI/ML lifecycle, including data protection, model access, application security, and AI supply‑chain risk.
- Assess the security of LLM‑powered applications, AI agents, and AI platforms.
- Identify and mitigate AI‑specific threats, including prompt injection, insecure output handling, data leakage, excessive agency, and model‑related vulnerabilities.
- Implement AI security guardrails, monitoring, adversarial testing, and secure integration patterns for AI‑enabled applications.
- Translate emerging AI security risks, standards, and regulatory requirements into practical architecture, policies, and engineering controls.
- Support the secure adoption of AI technologies across enterprise environments.
- Design and implement data security controls covering data classification, discovery, inventory, retention, and secure handling.
- Implement encryption, key management, tokenisation, data loss prevention (DLP), and data access governance.
- Strengthen security across databases, storage platforms, and structured and unstructured data environments.
- Collaborate with privacy, legal, and business stakeholders to ensure data processing complies with relevant regulatory and contractual obligati