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Ai Risk Analyst Jobs in Virginia (NOW HIRING)

AI Policy Analyst VBP

Virginia Beach, VA · On-site

$80K - $110K/yr

The City of Virginia Beach is seeking a highly analytical and principled AI Policy Analyst to guide ... The ideal candidate has a strong understanding of AI ethics, risk management, regulatory trends ...

Showing results 21-40

Ai Risk Analyst information

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$15

$40

$65

How much do ai risk analyst jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for ai risk analyst in Virginia is $40.14, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $48.85 per hour, depending on experience, location, and employer.

What is an AI risk analyst?

AI Risk Analysts are professionals who assess, monitor, and manage the risks associated with the development and deployment of artificial intelligence systems. Their work involves identifying potential threats such as bias, security vulnerabilities, ethical concerns, and compliance issues that could arise from using AI technologies. They collaborate with data scientists, engineers, and compliance teams to develop risk mitigation strategies and ensure that AI systems operate safely, ethically, and in accordance with relevant regulations.

What skills and qualifications are needed to be an AI risk analyst?

To thrive as an AI Risk Analyst, you need a strong foundation in data analysis, risk assessment, and an understanding of AI/ML technologies, typically supported by a degree in computer science, statistics, or a related field. Familiarity with risk management frameworks, AI auditing tools, and certifications such as CRISC or AI ethics credentials is often required. Excellent problem-solving, critical thinking, and communication skills help in identifying risks and conveying complex findings to stakeholders. These skills are crucial to ensure responsible AI deployment, mitigate potential risks, and maintain regulatory compliance.

How does an AI risk analyst collaborate with cross-functional teams to assess and mitigate risks?

AI Risk Analysts work closely with data scientists, engineers, compliance officers, and business leaders to identify, evaluate, and mitigate risks associated with AI systems. They facilitate risk assessment workshops, gather input from technical and non-technical stakeholders, and ensure that risk controls are integrated into AI development processes. Effective communication and documentation are crucial, as analysts must translate complex technical risks into actionable recommendations for diverse teams. This collaborative approach helps ensure that AI solutions are both innovative and aligned with regulatory and ethical standards.

What is the difference between Ai Risk Analyst vs Data Scientist?

AspectAi Risk AnalystData Scientist
Required CredentialsBachelor's in Risk Management, Data Science, or related fields; certifications in AI or risk analysisBachelor's or Master's in Data Science, Statistics, or Computer Science; certifications in data analysis or machine learning
Work EnvironmentFinancial institutions, insurance companies, or tech firms focusing on risk assessmentTech companies, research labs, or any industry leveraging data for insights
Employer & Industry UsagePrimarily in finance, insurance, and risk-focused sectorsAcross various industries including tech, healthcare, finance, and marketing

The main difference is that an Ai Risk Analyst specializes in assessing and managing risks related to AI systems, often within financial or risk-focused industries. In contrast, a Data Scientist analyzes large datasets to extract insights across diverse sectors. While both roles require strong analytical skills and knowledge of AI and data tools, the Ai Risk Analyst focuses more on risk mitigation specific to AI applications.

How much do AI risk analysts make?

AI risk analysts typically earn between $70,000 and $130,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in AI safety and risk management can earn higher salaries, often exceeding $150,000. The role often requires knowledge of AI systems, risk assessment, and relevant certifications.

How to become an AI risk analyst?

To become an AI risk analyst, candidates typically need a strong background in computer science, data analysis, or related fields, along with knowledge of AI systems and risk management principles. Relevant skills include programming, statistical analysis, and understanding of AI safety and ethics, often supported by certifications or advanced degrees. Experience with AI tools and risk assessment frameworks is also valuable.

What does an AI risk analyst do?

An AI risk analyst evaluates potential risks associated with artificial intelligence systems, including safety, ethical concerns, and unintended consequences. They analyze data, develop risk mitigation strategies, and often use tools like risk assessment frameworks to ensure AI deployment aligns with safety standards and regulations.

Will AI take over AI Risk Analyst jobs?

AI Risk Analysts evaluate and manage risks associated with artificial intelligence systems, a role that requires specialized knowledge of AI technologies, ethics, and safety protocols. While AI tools can assist in data analysis and risk assessment, human expertise remains essential for interpreting complex issues and making strategic decisions, so the job is unlikely to be fully automated in the near term.

What cities in Virginia are hiring for Ai Risk Analyst jobs?

Cities in Virginia with the most Ai Risk Analyst job openings:

Infographic showing various Ai Risk Analyst job openings in Virginia as of August 2026, with employment types broken down into 71% Full Time, 27% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $83,487 per year, or $40.1 per hour.

GDMS Senior AI Governance & Risk Specialist

Phase2 Technology

Chantilly, VA • On-site

$144.45 - $152/hr

Other

Posted 17 days ago


Key responsibilities

  • Conduct and lead comprehensive AI risk assessments and governance audits against emerging regulations for generative AI, LLM‑based, and agentic applications.

  • Evaluate and ensure adherence to government and corporate AI policies, standards, and regulations across multiple layers including AI inventory, data governance, security, model assurance, human oversight, and compliance.

  • Assess risks specific to agentic AI systems and multi‑agent architectures, including tool‑calling behavior, memory and retrieval systems, external API access, autonomous decision loops, and agent‑to‑agent communication patterns.


Job description

GDMS Senior AI Governance & Risk Specialist

Basic Qualifications: Bachelor's degree or equivalent, or the combination of education and relevant work experience with a minimum of 8 years of relevant experience; or Master's degree with a minimum of 6 years of relevant experience. U.S. citizenship is required.

Job Description

GDMS operates one of the largest enterprise AI deployments in the defense industry, with adoption spanning generative AI copilots, LLM‑powered applications, and a rapidly growing portfolio of agentic and autonomous AI systems. The governance challenge is keeping pace with a workforce that already uses AI while ensuring every deployment meets risk, security, and compliance standards that mission‑critical defense work demands.

As a Sr. AI Governance & Risk Specialist, you will be a core practitioner on the Agentic AI Governance team, executing day‑to‑day work that keeps GDMS AI deployment safe, accountable, and trusted. Responsibilities include conducting AI risk assessments, performing governance audits, evaluating adherence to regulations, leading corrective actions, and serving as subject matter expert for engineering and program teams navigating the AI lifecycle. You will work directly with agentic tools and applications, bringing firsthand understanding of their behavior and governance controls.

Key Responsibilities AI Governance Execution & Assessment
  • Conduct and lead comprehensive AI risk assessments and governance audits against emerging regulations for generative AI, LLM‑based, and agentic applications; document findings, risk ratings, and mitigation strategies, and lead corrective actions.
  • Evaluate and ensure adherence to government and corporate AI policies, standards, and regulations across the six layers: AI inventory and discovery; data governance; security and access controls; model assurance; human oversight; and compliance and audit.
  • Apply and maintain tiered governance frameworks calibrated to risk level, ensuring low‑risk use cases clear quickly while mid‑ and high‑risk applications receive appropriate scrutiny and escalation.
  • Maintain the enterprise AI use inventory and control framework, including system inventory, risk register, shadow AI detection, approved use catalog, and control mapping; support dashboard reporting and KPI monitoring for AI governance program health.
  • Prepare governance recommendations for approval and escalation, ensuring mid‑ and high‑risk AI systems are escalated with clear risk rationale and decision support materials.
  • Support development of self‑service governance tooling, checklists, and playbooks that enable program teams to adopt AI responsibly without requiring individual review for low‑risk applications.
Agentic AI Risk & Technical Assessment
  • Assess risks specific to agentic AI systems and multi‑agent architectures including tool‑calling behavior, memory and retrieval systems, external API access, autonomous decision loops, and agent‑to‑agent communication patterns.
  • Apply failure mode analysis to evaluate behavioral boundaries, unintended action risks, adversarial prompt vulnerabilities, and out‑of‑scope execution risks for agentic deployments.
  • Evaluate and document human‑in‑the‑loop (HITL) requirements and escalation thresholds appropriate to each agentic use case based on risk level, decision reversibility, and mission context.
  • Conduct hands‑on evaluation of agentic tools and platforms including AI coding assistants, copilot‑style applications, and multi‑agent orchestration frameworks to ground governance assessments in actual system behavior rather than vendor documentation alone.
  • Implement measures to monitor and mitigate risks associated with AI systems and data flows across GDMS IT and network infrastructure; investigate and manage responses to AI governance incidents and anomalies to prevent and mitigate exposure.
Policy, Standards & Regulatory Compliance
  • Maintain AI governance policies for responsible AI deployment, integrating government and corporate AI requirements into policy, standards, procedures, and operational guidance; own the policy lifecycle from drafting through review, approval, and periodic refresh aligned to enterprise risk priorities and evolving regulatory expectations.
  • Translate regulatory requirements, including NIST AI RMF, OWASP Top 10 for LLMs, MITRE ATLAS, the EU AI Act, applicable U.S. Executive Orders on AI, and ISO 42001, into clear, actionable internal controls and assessment criteria without creating bureaucratic drag.
  • Monitor the evolving domestic and international AI regulatory landscape; identify changes with organizational impact and elevate findings with recommended policy responses.
  • Coordinate with Privacy, Legal, Cybersecurity, and IT leadership to assess compliance risk against emerging AI regulations, including the EU AI Act, applicable U.S. Executive Orders on AI, and evolving DoD and federal AI policy; identify control gaps, quantify exposure, and recommend corrective measures before requirements become binding obligations.
  • Produce compliance reporting on AI controls for internal audit, regulatory examination, and governance committee review, documenting control effectiveness, open findings, and remediation status; support audit readiness activities including evidence collection, control validation, and documentation packages suitable for internal and regulatory stakeholders.
Risk Monitoring, Reporting & Controls Assurance
  • Perform ongoing monitoring and validation of deployed AI systems, including review of model performance, drift indicators, bias signals, and continued alignment with approved use scope.
  • Identify opportunities to apply AI and automation for continuous improvement of the AI governance program itself, including automated risk attribution, KPI tracking, and telemetry‑driven evidence.
  • Generate AI risk and governance reporting contributing to dashboards, risk posture summaries, and periodic reports for program leadership and cross‑functional stakeholders.
  • Evaluate effectiveness of cybersecurity controls applied to AI systems (NIST CSF, NIST AI RMF), collaborating with the Cybersecurity organization to integrate governance without duplicating ownership.
  • Support vendor and third‑party AI risk assessments, ensuring AI components from external providers meet GDMS contractual, regulatory, and governance requirements.
Knowledge, Skills & Abilities
  • Collaborates and works effectively cross‑functionally throughout the business, including with Legal, Information Technology, Cybersecurity, Security, and Contracts organizations.
  • Excellent computer and data management knowledge, including IT Security, Cybersecurity, and cloud infrastructure concepts as they apply to AI system risk.
  • Excellent ability to communicate comfortably with senior management, translating complex AI risk and governance topics into clear, decision‑ready information.
  • Excellent ability to manage a risk profile and design effective mitigation strategies appropriate to AI and agentic system risk scenarios.
  • Working knowledge of NIST AI RMF, OMB AI guidance, FAR/DFARS AI requirements, DoD Responsible AI principles, CMMC implications, EU AI Act, GDPR, UK AI Governance Framework, ISO 42001, OECD AI Principles, and emerging state laws (Colorado, California, Virginia, Texas).
  • Hands‑on familiarity with Microsoft Copilot, Microsoft Purview, OpenAI, Anthropic, Google, and open‑source AI ecosystems; awareness of Palantir, Snowflake, Databricks, ServiceNow.
  • Understanding of Agentic AI 7‑layer operating model, MCP architectures, tool calling, agent‑to‑agent communications, and human approval gates.
  • Excellent analytical, written, and presentation skills; demonstrated ability to produce governance documentation, policy materials, and stakeholder briefings of high quality.
Education and Experience
  • Bachelor's degree or equivalent is required, or the combination of education and relevant work experience, plus a minimum of 8 years of relevant experience; or
  • Master's degree plus a minimum of 6 years of relevant experience in AI governance, technology risk, cybersecurity GRC, responsible AI, or AI/ML compliance.
  • Certifications highly sought include IAPP AI Governance Professional (AIGP), Certified Risk and Information Systems Control (CRISC), Advanced AI Risk (AAIR), ISO 42001.

Target salary range: USD $144,451.00/Yr. - USD $152,000.00/Yr.

Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans

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