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

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

Develop methods to monitor and measure risk and assurance efforts on a continuous basis.

Perform AI architecture security reviews, identify gaps, and develop a risk management plan to address issues.

Knowledge of emerging security issues, risks, and vulnerabilities.

Ensure risk mitigation plans of action and milestones are in place.

Ensure that AI design and development activities are properly documented and updated.

* 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.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Communicate the results of AI risk assessments to relevant stakeholders.

Coordinate with appropriate personnel to identify methods for users and developers to report concerns about the implementation of DoD AI Ethical Principles.

Create and/or maintain processes to ensure data management efforts comply with AI ethical principles.

Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied.

Develop risk mitigation strategies to ensure enumerated risks are prioritized, mitigated, shared, transferred, and/or accepted.

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

Ensure risk management responsibilities are clearly defined, assigned, and communicated to relevant stakeholders.

Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.

Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws.

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

Measure the compliance of AI tools with DoD AI Ethical Principles.

Perform risk assessment on AI applications to identify technical, societal, organizational, and mission risks.

Knowledge of machine learning theory and principles.

* Knowledge of specific operational impacts of cybersecurity lapses.

* 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.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation 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 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 Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of the AI lifecycle.

Knowledge of the benefits and limitations of AI capabilities.

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

Knowledge of the principles, methods, and tools used for risk and bias assessment and mitigation, including assessment of failures and their consequences.

Skill in assessing AI capabilities for bias or ethical concerns.

Skill in developing solutions and/or recommendations to minimize negative impacts of machine learning, especially for edge cases.

Skill in explaining AI concepts and terminology.

Skill in identifying low-probability, high-impact risks in machine learning training data sets.

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

Skill in identifying risk over the lifespan of an AI solution.

Skill in testing and evaluating machine learning algorithms or AI solutions.

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

Perform risk assessment whenever an AI application or AI-enabled system undergoes a major change, when emergent behaviors are detected, and/or unintended consequences are reported.

Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.