Job Description

We are building a governance, risk, and compliance function to enable our company to build

products that can withstand regulatory scrutiny, and ensure Meta continues to meet global

regulatory requirements and manage risk. Meta's Risk and Compliance Program (RCP) is the central engine driving risk management and compliance at the company, supporting Meta and the family of apps. Within RCP, the Security Risk Program (SRP) is the second-line function accountable for how Meta identifies, assesses, quantifies, and reports its security risk posture — delivering global security risk assessments, Capability Maturity & Effectiveness (CME) evaluations, AI and cloud risk assessments, and board-level and regulatory reporting.

We are seeking a Security Risk Program Manager to build one of the program's highest-priority new capabilities: security risk assessment of AI product launches. Today, AI launches receive

ad-hoc coverage through security risk assessments designed for infrastructure — not for

product-launch cadence, and not for cross-domain AI risk spanning Security, Privacy, Integrity,

and Legal. You will design that capability from the ground up and scale it from roughly five

assessments per quarter today to twenty or more per quarter by H1 2027, in step with Meta's AI

product velocity.

This is a builder and an influencer role in equal measure. AI launch risk cannot be

assessed by one function acting alone — it requires Central Security, product groups, Privacy,

Integrity, and Legal moving through a shared process on a launch timeline. You will own that

operating model: translating product and engineering reality into a defensible risk position, and

translating regulatory obligation into assessment work that product teams can actually absorb

without stalling a launch. The ideal candidate is comfortable with ambiguity, effective in

high-pressure and fast-moving situations, and able to build durable relationships across a wide

range of technical and non-technical stakeholders.

Responsibilities

Design and implement Meta's AI Launch Risk Assessment framework end to end — intake criteria, cross-domain risk taxonomy, assessment methodology, and launch-gate outputs — and scale delivery from ~5 to 20+ assessments per quarter by H1 2027.

• Build and partner to manage the XFN operating model that coordinates Central Security, product groups, Privacy Risk Management, Integrity Risk Management, and Legal through each assessment, with clear roles, handoffs, and decision rights on a launch timeline.
• Partner directly with product and engineering teams on high-priority AI launches — engaging early enough that security risk review informs design decisions rather than gating release, and giving product teams a predictable, well-documented path through second-line review.
• Serve as the connection point between the Security Risk Program, Central Security leadership, Legal, and first-line business teams, representing security risk positions and negotiating assessment scope, sequencing, and remediation commitments.
• Build and maintain a consolidated AI risk register that gives leadership a single view of.
• security risk across the AI product portfolio, and produce the reporting that keeps that view current for VP and executive audiences.
• Define tooling requirements and drive AI-enabled automation across the assessment lifecycle — identifying where automation can scale throughput without compromising assessment defensibility, and partnering with tooling and engineering owners to deliver it.
• Ensure assessments and supporting artifacts are produced to internal standards, are.
• submission-ready, and constitute defensible evidence of systematic security due diligence for regulators and auditors (including EU AI Act and NIST AI RMF expectations).
• Identify risks, dependencies, and blockers across the program, define mitigation plans, monitor key delivery and SLA metrics, and drive corrective action with stakeholders when performance deviates.
• Create and facilitate presentations that build senior leadership understanding and influence decision-making, and provide mentorship and guidance to junior team members on program design and assessment practice.

Qualifications

8+ years of experience in governance, risk, and compliance, security risk management,

• regulatory compliance, or a directly related discipline
• 3+ years of program management experience in a corporate environment
• Experience with risk and compliance precepts, practices, and solutions
• Demonstrated experience designing a program or process from the ground up — not solely running an established one — including methodology, operating model, and rollout
• Demonstrated experience driving cross-functional alignment across technical and non-technical partners without direct authority, including with engineering or product organizations
• Proven verbal and written communication skills, with success influencing a range of audiences including senior leadership Experience assessing or managing risk for AI or machine learning systems, or familiarity with AI-specific regulatory and risk frameworks (EU AI Act, NIST AI RMF)
• Experience embedding risk, security, or compliance review into a fast-moving product
• development lifecycle and launch process
• Experience working in information security and/or cybersecurity
• Experience partnering with a central security or infrastructure security organization as a
• second-line risk function
• Experience defining tooling requirements or applying automation and AI to scale GRC operations
• Knowledge of global regulatory frameworks, compliance practices, and risk management best
• practices
• experience with risk assessments, regulatory responses, audits, or control design
• 3+ years working in a corporate environment subject to audit against federal or industry-wide regulations
• Experience in a technology, financial services, consulting, or related field
• Advanced degree and/or relevant certification (CISSP, CRISC, CISM, CISA)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies