Job Description
Job title:AI Security Manager / Architect
Role Summary
Philips Enterprise Information Security is responsible for security controls across enterprise IT. As Artificial Intelligence (AI), Machine Learning (ML), and Generative AI technologies become core components of modern healthcare solutions, Philips must ensure these capabilities are implemented securely and in compliance with global healthcare regulations and Philips AI Principles.
The position will lead the strategy for securing AI platforms across Philips. The role will define enterprise AI security architecture, governance frameworks, and security standards that support secure development and deployment of AI solutions. This role will collaborate with Global AI Center of Practice to ensure that AI innovation aligns with patient safety, regulatory compliance, and enterprise security standards.
AI Security Architecture
- Design and maintain the enterprise AI Security Reference Architecture aligned with enterprise architecture frameworks. The architecture will define secure patterns for machine learning pipelines, data pipelines, model training environments, inference services, and Generative AI platforms deployed across Philips.
u00A0 - Support modern AI platforms such as Azure OpenAI, AWS BedRock, Google Gemini AI, and Databricks ML environments while ensuring secure integration with enterprise data platforms and applications. Secure design principles such as Zero Trust for AI systems, model isolation, secure API integrations, and protected AI workloads will be incorporated into enterprise architecture standards.
AI and GenAI Security Controls
- Define enterprise security controls for AI and Generative AI environments including secure model registries, model artifact integrity validation, cryptographic model signing, and verification of model provenance.
- The architect will establish security guardrails for Large Language Model (LLM) deployments including prompt injection protection, output filtering, content moderation, protection against sensitive data leakage, and secure implementation of Retrieval Augmented Generation (RAG) architectures.
- Address emerging risks such as model extraction, adversarial attacks, and inference manipulation.
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LLM Governance and AI Platforms
- Establish governance models for enterprise use of foundation models and generative AI technologies.
- Define architectural guardrails for AI orchestration frameworks and implement safety mechanisms. Governance policies will ensure responsible AI usage, secure prompt management, and compliance with enterprise data protection requirements.
AI Threat Modeling and Advanced Security
- Lead AI threat modeling and risk assessments using frameworks such as MITRE ATLAS, OWASP Top 10 for LLM Applications, and the NIST AI Risk Management Framework.
- Address advanced AI security areas including AI supply chain security, model provenance verification, and security controls for autonomous AI agents.
AI Security Monitoring and Observability
- Design monitoring capabilities for AI systems to ensure continuous oversight of model behavior, performance, and security posture.
Regulatory Compliance
- Ensure that enterprise AI systems comply with global healthcare regulatory requirements including the EU AI Act.
- Support regulatory expectations for explainability, transparency, fairness, auditability, and human oversight in AI-driven systems.
AI Security Center of Excellence and Governance
- Establish and lead an enterprise AI Security Center of Excellence responsible for defining AI security standards, architecture guidelines, security testing practices, and governance processes.
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AI Security Maturity Model
- Develop and maintain an enterprise AI Security Maturity Model that guides the organizationu2019s journey toward secure AI adoption focusing on automated monitoring, continuous model validation, and AI-driven security analytics across enterprise AI platforms.
Preferred AI Security Tooling
- Experience with emerging AI security platforms and tooling such as Lakera, ProtectAI, HiddenLayer, NVIDIA AI security stack, Garak LLM vulnerability scanner, and Microsoft PyRIT for AI red teaming is highly desirable.
Qualifications
Minimum 12+ years of experience in cybersecurity or information security with at least 6+ years in enterprise security architecture roles. Demonstrated experience securing AI platforms. Strong knowledge of cloud platforms including Microsoft Azure, AWS, or Google Cloud and experience designing security architecture for modern data and AI platforms.
Preferred Certifications
CISSP, CCSP, SABSA, or ISSAP certifications are preferred. Additional certifications in AI governance, Responsible AI, cloud security architecture, or AI risk management are considered an advantage.
How we work together
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.
Onsite roles require full-time presence in the companyu2019s facilities.
Field roles are most effectively done outside of the companyu2019s main facilities, generally at the customersu2019 or suppliersu2019 locations.
This role is an office role.
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About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.
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If youu2019re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care .
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