Responsibilities

The AI Cybersecurity Architect is Altec's enterprise strategy leader and visionary in AI cybersecurity and data protection. This role is accountable for defining and driving a robust security architecture that safeguards the data and systems underlying all AI initiatives. The AI Cybersecurity Architect ensures that every facet of the AI ecosystem is secure by design - guarding against external AI-driven threats, governing internal AI usage and custom AI applications, and protecting the data pipelines that fuel AI solutions. Operating at the intersection of cybersecurity and innovation, the Architect partners with senior IT leadership (including the SVP of Information Services, VP of IT Operations, & Head of Cybersecurity) and collaborates with the AI Center of Excellence and Advanced Development team to embed cybersecurity into every AI pilot, agent workflow, and data process. By doing so, the AI Cybersecurity Architect enables Altec to accelerate AI adoption safely, maintaining rigorous security and control over valuable data even as the organization rapidly expands its use of AI.

Key Responsibilities

AI Security Architecture

• Define and own the enterprise AI security strategy and architecture: Develop a comprehensive approach for securing AI technologies across the organization, addressing both external AI-driven threats and internal AI use.
• Architect and oversee robust AI security controls: Ensure the design and implementation of enterprise-wide AI security controls and guardrails.
• Establish technical guardrails against AI-specific threats: Define and implement protective mechanisms to defend against risks unique to AI.
• Secure AI agents and integration points: Provide architecture leadership for the safe design of AI agent frameworks, Model Context Protocol (MCP) servers, and model gateway integrations.
• Counter AI-enabled threats with a blue-team mindset: Continuously evaluate and anticipate emerging threats from malicious AI usage. Drive the incorporation of AI-related threat intelligence into the security program.

AI Risk, Governance & Strategy

• Establish enterprise AI security policies and standards: Define and maintain enterprise-wide standards for AI data handling, logging, monitoring, identity, and access management for AI systems.
• Lead AI security governance and risk management: Own the AI security governance framework, ensuring alignment with overall cybersecurity and business strategy.
• Drive AI risk assessment and review processes: Architect a rigorous risk assessment program for AI projects and usage. Lead the security review of new AI tools, pilots, and proofs of concept in collaboration with the AI innovation teams, setting the criteria for evaluation and required security controls.
• Align with NIST 800-53 and emerging AI frameworks: Ensure that all AI security controls, policies, and architectures align with the company's adherence to NIST 800-53 and relevant AI risk management frameworks (such as NIST AI RMF).
• Metrics-driven oversight and executive reporting: Define key metrics and generate executive-level reporting on AI security posture, usage risks, and data protection effectiveness.
• Lead AI-related incident response & learning: Oversee the incident response strategy for AI-related security incidents, such as inadvertent data exposure through AI tools, malicious AI agent behaviors, or external AI-driven attacks.

Data Protection & Data Loss Prevention (DLP) Architecture

• Architect enterprise data protection and DLP controls: Serve as the technical owner of Data Loss Prevention (DLP) and data security architecture across the company's IT and OT environments.
• Enhance detection and response for data security events: Architect the integration of DLP and Data Security Posture Management telemetry with security monitoring and incident response platforms (e.g., SIEM, SOC workflows).
• Drive continuous improvement in data security: Continuously evaluate and advance the maturity of the organization's data security in line with strategic goals.

Collaboration & Executive Enablement

• Strategic liaison to AI and business teams: Act as the primary cybersecurity liaison and advisor for teams developing or deploying AI solutions, including R&D and advanced technology groups. Facilitate open communication so that security requirements become enablers for innovation rather than barriers, empowering teams to use AI responsibly and safely.
• Influence and partner with executive leadership: Work closely with the Head of Cybersecurity, SVP of Information Services, VP of IT Operations, and other senior stakeholders to drive alignment on AI security strategy and risk management. Provide thought leadership and expert guidance to inform executive decision-making on AI-related investments, policies, and risk appetite.
• Player/coach leadership and mentorship: Operate as a player-coach - while primarily focusing on high-level strategy and design, be willing to get hands-on to solve complex problems or build initial solutions where necessary. Develop proof-of-concepts for new AI security controls or processes to demonstrate their value, then lead and mentor cybersecurity engineering teams to scale these solutions. Guide and upskill junior security engineers and analysts, sharing expertise and building a strong bench of technical talent.
• Cultivate a red-team and innovation mindset: Foster a culture of continuous improvement and adversarial thinking within the cybersecurity team and across the organization. Encourage questioning of assumptions and rigorous validation of AI security controls, e.g., through regular red-team exercises, "break-it" testing of AI systems, and challenging vendor claims. Promote creative problem solving and out-of-the-box approaches to stay ahead of emerging AI threats, ensuring that the company's AI security capabilities remain resilient and forward-looking.
• Education and enablement for secure AI use: Champion organization-wide education on secure AI practices. Work with training teams to deliver targeted guidance and awareness programs for developers, data scientists, and business users on how to incorporate AI solutions securely and protect sensitive data when using AI.

Required Education, Experience, and Skills

• Bachelor's degree in Computer Science, Information Security, Information Systems, Engineering, or a related and 6+ years of progressive cybersecurity experience with a bachelor's degree; OR 8+ years of relevant experience without a degree, including at least 3+ years in a senior cybersecurity architecture or strategic leadership role.
• An advanced degree (e.g., Master's in Cybersecurity or Data Science) strongly preferred.
• Proven expertise in architecting and implementing security controls across multiple domains, such as data protection/DLP, cloud security, identity & access management, application security - with significant focus on AI/ML security as a critical domain.
• In-depth knowledge of Generative AI and Large Language Model technologies, including AI agent frameworks and the unique security risks they introduce (e.g., prompt injection, data leakage, model poisoning, insecure plugin integrations, malicious output manipulation).
• Expert-level understanding of enterprise data protection concepts (classification, labeling, encryption, tokenization, rights management) and hands-on experience designing and implementing Data Loss Prevention (DLP) controls at scale across email, endpoints, cloud (Office 365/SaaS), and other collaboration platforms.
• Experience designing advanced security monitoring and threat detection solutions, including integrating diverse security telemetry (e.g., DLP events, cloud logs, AI usage data) into SIEM/SOC workflows and detection pipelines to enable rapid incident response.
• Strong background in secure cloud and identity architectures: Familiar with multi-cloud platforms (Azure, AWS, OCI), modern identity and access protocols (OAuth2, OIDC, SAML), and API security fundamentals - and how to apply them in securing AI services and data flows.
• Deep familiarity with NIST 800-53 controls and security frameworks relevant to manufacturing, as well as working knowledge of emerging AI risk management frameworks (e.g., NIST AI RMF); able to align technical architectures and controls to meet these standards and regulatory requirements.
• Exceptional communication skills, both written and verbal - capable of translating complex AI and data security risks into clear business terms and actionable guidance for executive leadership and non-technical stakeholders.
• Proven leadership and collaboration abilities - able to independently drive cross-functional initiatives, working effectively with Information Security, IT, and business teams. Demonstrated talent for mentoring and developing technical staff and operating in a "player/coach" capacity (strategic visionary who can also dive deep and prototype solutions when needed).
• Innovative, adversarial mindset - a "red team" thinker who questions assumptions, creatively tackles problems, and rigorously stress-tests security controls to continually strengthen the organization's AI security posture.

Preferred / Beneficial Qualifications

• Proven track record evaluating, selecting, and integrating enterprise-scale AI security and data protection solutions (e.g., AI Security Platforms, AI governance tools, Data Security Posture Management, CASB, enterprise DLP); capable of critically assessing emerging vendor offerings and aligning chosen technologies with the organization's security architecture and risk requirements.
• Extensive experience securing complex AI ecosystems - including custom-built AI applications, generative AI agent frameworks, retrieval-augmented generation (RAG) pipelines, and model gateways - by designing robust security guardrails and leading AI red-teaming exercises to validate and continuously improve protective measures.
• Proven ability to design security architectures that accommodate international data protection and privacy requirements (e.g., GDPR) across multi-tenant cloud and hybrid environments, ensuring AI and data systems adhere to global compliance standards and data residency obligations.
• In-depth understanding of manufacturing and industrial control (OT/ICS) environments, enabling adaptation of AI security and data protection strategies to the unique needs and constraints of operational technology systems.

Preferred Certifications

• CISSP (ISC2) - Certified Information Systems Security Professional, demonstrating broad security expertise
• CCSP (ISC2) - Certified Cloud Security Professional, reflecting cloud security proficiency
• AI/ML-focused security credentials, such as ISC2's Certified in AI Security, IAPP's AI Governance Professional, or SANS' AI Security certificate, highlighting specialized knowledge in AI risk and governance.
• Other relevant certifications, such as CISM (Certified Information Security Manager), CCSK (Certificate of Cloud Security Knowledge), Microsoft SC-100 (Cybersecurity Architect), Microsoft SC-400 (Information Protection Administrator), or vendor-specific DLP/CASB/AISP platform certifications.

Experience Level Adjustmnt

Should the selected candidate meet the qualifications of a more experienced level in the career path, the job level may be adjusted.

Benefits

Altec offers a competitive salary that rewards performance and dedication, along with a comprehensive benefits package that includes:

• Medical, Dental, Vision and Prescription Drug Program
• Retirement 401(k) Traditional or Roth Program Options with Company Match
• Vacation and Holidays
• Parental Leave
• Short Term and Long Term Disability Leave
• Flexible Spending Accounts
• Tuition Assistance Program
• Employee Assistance and Mental Health/Substance Abuse Program
• Life Insurance, Accidental Death and Dismemberment Insurance
• Supplemental Insurance including Hospital Indemnity, Critical Illness and Accident Insurance
• Additional Wellness Programs and Rewards Available