* Knowledge of computer networking concepts and protocols, and network security methodologies.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Acquire and manage the necessary resources, including leadership support, financial resources, infrastructure, and key personnel, to support AI innovation adoption goals and objectives.

Advise senior management on risk levels, security posture, and necessary changes to existing AI policies.​

Design and integrate an AI adoption strategy that supports the organization’s vision, mission, and goals.

Develop and maintain strategic plans.

Identify and address AI workforce planning and management issues (e.g., recruitment, retention, and training).

Oversee AI budget, staffing, and contracting decisions.

Knowledge of the organization’s core business/mission processes.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Facilitate interactions between internal and external partner decision makers to synchronize and integrate courses of action in support of objectives.

Maintain relationships with internal and external partners involved in cyber planning or related areas.

Conduct long-range, strategic planning efforts with internal and external partners to support AI capability development and use.

Knowledge of organization objectives, leadership priorities, and decision-making risks.

Analyze national security/DoD mission priorities and gaps suitable for the application of AI solutions.

Appoint and guide a multidisciplinary team of AI experts to identify and assess risk throughout the AI development lifecycle.

Assess value of implemented AI projects based on organizational metrics.

Create and/or maintain governance structure for oversight and accountability of AI solutions.

Direct and/or support organizational and project-level AI risk management activities.

Engage and collaborate with allies and partners to advance shared strategic AI objectives.

Establish and/or maintain processes to ensure Responsible AI practices are reflected in an organization’s approach to AI acquisition, development, and deployment.

Evaluate and develop AI workforce structure resources and requirements.

Identify and address key roadblocks to AI implementation.

Identify viable AI projects based on organizational needs.

Identify ways to lead and motivate people to adopt AI solutions through cultural, organizational, or other types of change.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Promote awareness of AI limitations and benefits.

Remove barriers to data acquisition, collection, and curation efforts required for AI solutions.

Ability to assess and forecast manpower requirements to meet organizational objectives.

Knowledge of Workforce Framework, work roles, and associated tasks, knowledge, skills, and abilities.

Knowledge of machine learning theory and principles.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Ability to identify, connect, and influence key stakeholders to speed AI adoption.

Ability to inspire and lead a culture of innovation.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of best practices in organizational conflict management.

Knowledge of data acquisition, collection, and curation best practices required for AI solutions.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI is developed and operated.

Knowledge of interactions and integration of DataOps, MLOps, and DevSecOps in AI.

Knowledge of resources and capabilities required to complete AI projects.

Knowledge of staffing, contracting, and budgetary requirements to run an AI-enabled organization.

Knowledge of the AI lifecycle.

Knowledge of the basic requirements for the successful delivery of AI solutions.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the nature and function of technology platforms and tools used to create and employ AI.

Skill in communicating AI and/or machine learning solutions to a wide range of audiences.

Skill in developing and influencing policy, plans, and strategy in compliance with laws, regulations, policies, and standards in support of organizational AI activities.

Skill in explaining AI concepts and terminology.

Skill in identifying organizational and project-level AI risks, including AI security risks and requirements.

Skill in leading AI adoption efforts.

Skill in leveraging and optimizing resources required to complete AI projects and programs.

Establish and collect metrics to monitor and validate AI workforce readiness.

Define and/or implement policies and procedures to enable an AI risk assessment process and assess risk mitigation efforts.

Monitor and evaluate the organization’s use of AI to ensure capabilities are performing as intended and to reduce the likelihood and severity of unintended consequences.

Recommend updates to military strategy and doctrine with respect to advances in AI technology, legal obligations, Responsible AI, and DoD AI Ethical Principles.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

Knowledge of AI-specific acquisition models (e.g., pay per use or per data element).

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of the possible impacts of machine learning blind spots and edge cases.