AI Information Security Engineer
Tenable · US - Headquarters - Maryland - Columbia
About this role
**Who is Tenable?** Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65% of the Fortune 500, 50% of the Global 2000, and large government agencies. **What makes Tenable such a great place to work?** Ask a member of our team and they’ll answer: **“Our people!”** We work together to build and innovate best-in-class cybersecurity solutions—while creating a culture of belonging, respect, and excellence. **Work Authorization** Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment (including, but not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc.). --- ## Your Role As an **AI Security Engineer** on Tenable’s Information Security team, you will help shape and mature our approach to securing AI—both within Tenable’s products and across the enterprise. AI introduces a new class of risk at the intersection of application security, cloud risk, and data governance, and you’ll help define how we assess, test, and address it. You’ll apply hands-on AI engineering experience alongside deep product security and application security skills to: - Design security controls for AI systems - Lead assessments - Help engineering teams ship AI features securely You will also establish security standards for responsible AI use, influence decisions across the organization, and build tooling that raises our security posture. This role is ideal for a practitioner who has spent real time building with AI systems, looking under the hood of model internals, and wants to channel that experience into a security specialty. ## Your Opportunity - Design, implement, and maintain end-to-end security controls across the AI/ML lifecycle. - Conduct safety research, inspecting model internals (e.g., detecting hidden deception, latent knowledge, or unwanted concepts across layers) as a core methodology. - Perform AI safety assessments and threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. - Design and implement robust security guardrails, controls, and boundary mechanisms for Large Language Models (LLMs), Small Language Models (SLMs), and open-source models. - Act as a subject matter expert in AI safety research by inspecting model internals to detect hidden deception, latent knowledge, or unwanted concepts, ensuring...
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