AI Cybersecurity Director

Evolution Cloud Services · Denver · $195,000–$210,000 (stated in the posting)

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AI Cybersecurity Director

Cybersecurity Department | Hybrid: Denver, CO or Ashburn, VA (3 days onsite, 2 days flexible per week)

EVOCS Overview

EVOCS's journey began with a mission to empower businesses with advisory expertise, empowered with ideal technologies to provide them with comprehensive solutions to grow and prosper.

Founded by a team of passionate experts, EVOCS has grown into a trusted partner to a growing number of leaders across their respective industries. Our roots in employee-managed operations reflect our commitment to quality, consistency, and client success.

If you enjoy working in a hyper-fast-growing company, are eager to be part of an agile team, and want to be part of our success story, then let's talk!

🎯 Role Overview

As the AI Cybersecurity Director, you will serve as the technical and security authority for artificial intelligence (AI), machine learning (ML), and large language model (LLM) systems deployed across a global data center and critical infrastructure operator's operational, OT, and enterprise environments.

You will be responsible for ensuring AI systems are architected, deployed, and operated with appropriate controls for data protection, model integrity, access governance, monitoring, and human-in-the-loop decision enforcement -- so AI delivers business value without introducing unacceptable cyber, operational, safety, workforce, or regulatory risk.

🧩 What You Will Do

Essential Functions

• Establish enterprise governance for detection, classification, and management of unauthorized (shadow) AI across business units, in coordination with centralized AI functions
• Define and enforce security architecture standards for AI, ML, and LLM platforms across cloud, hybrid, on-prem, and OT-adjacent environments
• Provide security design oversight and approval for AI systems, including data pipelines, model hosting, inference paths, APIs, and integrations
• Define enterprise methodology for AI security assessment covering architecture, design, and implementation across applications, agents, and workflows
• Ensure AI architectures enforce segmentation, least privilege, deterministic behavior, and fail-safe operation, particularly where OT or critical infrastructure data is involved
• Establish AI-specific incident response playbooks and lead response to AI-related security, safety, or governance incidents
• Enforce controls preventing unauthorized model retraining, autonomous learning, or use of live production or OT data outside approved intent
• Define security requirements for explainability, traceability, and output validation where AI influences operational, workforce, safety, or compliance outcomes
• Drive alignment with ISO 42001 and related AI governance standards across applicable teams

AI Data Protection and Trust Boundaries

• Enforce protections against prompt injection, data leakage, hallucination risk, unauthorized context expansion, and external model training exposure
• Ensure sensitive enterprise, operational, personnel, and contractual data is not exposed to or retained by external AI platforms without approved safeguards
• Approve and oversee AI data ingestion pipelines, enforcing purpose limitation, data minimization, and classification requirements
• Validate encryption, access logging, retention, and deletion controls for data used by AI systems
• Define and enforce controls preventing cross-domain data correlation that violates trust boundaries or governance constraints

AI Threat, Risk, and Monitoring Management

• Perform AI-specific threat modeling, including risks such as data poisoning, model theft, inference abuse, output manipulation, and decision integrity compromise
• Integrate AI threats into enterprise cybersecurity and OT risk models, including definition of compensating controls and escalation for systems exceeding risk tolerance
• Own and maintain the AI risk register covering confidentiality, integrity, availability, explainability, data quality, model drift, adversarial attacks, and business impact
• Ensure AI systems generate telemetry, logging, and audit trails sufficient to detect misuse, drift, or anomalous behavior
• Integrate AI security monitoring into SOC, SIEM, and enterprise incident response workflows

OT and Critical Infrastructure Safeguards

• Enforce prohibitions on autonomous AI control of OT assets, including power, cooling, building management systems, fire suppression, and physical access systems
• Validate one-way data flows, read-only access models, and manual override requirements where AI consumes OT telemetry
• Partner with OT and infrastructure teams to ensure AI enhances visibility and decision support without compromising safety, reliability, or uptime
• Oversee security reviews of vendor-provided and embedded AI capabilities, including model behavior, data handling, and contractual protections
• Define and enforce minimum security and governance requirements for AI vendors, including audit rights and termination conditions

🧠 What You Will Bring

The top candidate will have the following qualifications:

• Bachelor's degree in Cybersecurity, Computer Science, Data Science, Engineering, or a related field, or equivalent experience
• Minimum 10+ years of experience in cybersecurity, security architecture, or risk engineering roles
• Hands-on experience securing data pipelines, APIs, cloud platforms, and analytics or ML-enabled systems
• Strong understanding of identity, access management, encryption, logging, and secure system design
• Fluent in English (written and spoken) -- bilingual or near-native level
• Strong interpersonal and communication skills -- comfortable engaging executives, engineers, and cross-functional stakeholders via email, calls, and meetings

Key Skills & Competencies

• AI and machine learning security
• LLM and generative AI risk management
• Security architecture and threat modeling
• Data protection and access governance
• Incident response and forensic analysis
• Cross-functional technical leadership

Ideally you have...

• Direct experience securing AI/ML platforms, LLMs, or analytics pipelines
• Experience with cloud security (Azure, AWS, GCP) and SaaS-based AI platforms
• Familiarity with OT, critical infrastructure, or safety-critical environments
• Security certifications such as CISSP, CCSP, CISM, or cloud security certifications

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