AI Information Security Engineer

Tenable•Columbia, MD

8d•Hybrid

About The Position

As an AI Security Engineer on Tenable's Information Security team, you will help shape and mature the company's approach to securing AI in its products and across the enterprise. The role addresses risks at the intersection of application security, cloud risk, and data governance. You will use hands-on AI engineering experience alongside product and application security skills to design security controls for AI systems, lead assessments, and help engineering teams ship AI features securely. You will establish standards for responsible AI use, influence organizational decisions, and build tooling to improve security. The position suits a practitioner who has experience building or exploring AI systems and wants to apply that experience to security. You will design and maintain controls across the AI/ML lifecycle; develop guardrails and reference architectures for language models, MCP servers, and agentic AI; assess AI systems for safety and security risks; and collaborate with engineering and product teams to integrate security into AI development and deployment. You will also create internal guidance and training, develop security metrics, and research emerging AI security trends. Tenable describes itself as the Exposure Management company, relied on by more than 40,000 organizations worldwide. The company emphasizes a culture of belonging, respect, and excellence, and offers comprehensive benefits. This position's U.S. base pay range is $102,500–$136,500; compensation may vary based on qualifications, skills, competencies, location, and experience, and eligible employees may receive variable compensation.

Requirements

At least 2 years of professional experience in information security or application security, with a demonstrated interest in securing AI/ML systems.
Awareness of AI-specific security threats, including adversarial ML, data poisoning, prompt injection, model inversion, model evasion, and inference attacks.
Knowledge of data security principles, including encryption, masking, and tokenization.
Knowledge of SSDLC, DevSecOps, and security testing practices, including SAST, DAST, SCA, and threat modeling.
Knowledge of application security architecture best practices and patterns, including modern web applications, Docker, and microservices.
Ability to work cross-functionally across engineering, product, and business teams.
Strong written and verbal communication skills, including the ability to explain complex AI security risks to technical and non-technical audiences.
Strong problem-solving skills, attention to detail, and ability to lead projects with end-to-end ownership.
Self-motivation and effectiveness working independently and across distributed teams.

Nice To Haves

Bachelor’s degree in Computer Science, Cybersecurity, Data Science, or a related field is preferred.
Hands-on experience with AI red teaming, adversarial prompt testing, or LLM security assessments.
Basic coding experience in Python and/or Go.
Experience with PyTorch, Hugging Face Transformers, TensorFlow, or similar frameworks and libraries.
Strong understanding of the ML/AI lifecycle and its associated security risks.
Experience with application security assessment tools such as Burp Suite, ZAP, or Tenable WAS.
Knowledge of cloud security platforms such as AWS, Azure, or GCP, including AI/ML-related services.
Security certifications such as CISSP, CSSLP, SANS GIAC, CEH, or AI-focused security certifications; these are a plus but not necessary.

Responsibilities

Design, implement, and maintain end-to-end security controls across the AI/ML lifecycle.
Assist with implementing security guardrails, controls, and boundary mechanisms for LLMs, SLMs, and open-source models.
Monitor the AI landscape for vulnerabilities and attack techniques and apply engineering expertise to defend enterprise AI deployments.
Assist in developing security reference architectures for AI deployment patterns, including MCP servers and agentic AI workflows and harnesses.
Deploy controls for model integrity, governance, and access control across models and feature stores.
Build AI-driven cybersecurity tooling for identity, incident management, vulnerability management, third-party risk, and emerging AI threat vectors.
Perform AI safety and security assessments and threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks.
Collaborate with software engineers and product teams to integrate security best practices into AI development, training, validation, and deployment.
Create security guidance, documentation, and training for internal engineering and development teams.
Develop metrics that encourage desired security behaviors and outcomes.
Research emerging AI security trends and contribute solutions supporting Tenable's AI initiatives.

Benefits

Medical, dental, vision, disability, and life insurance
401(k) retirement savings with company match
Employee stock purchase plan
Employee referral program
Flexible spending accounts
Employee Assistance Program (EAP)
Education assistance
Parental leave
Paid time off (PTO)
Company-paid holidays
Health and wellness events
Community programs
Variable compensation for eligible employees, including bonuses for non-sales roles