From Reaction to Prevention: WPS and Artificial Intelligence

Executive Summary

Artificial intelligence, when intentionally aligned with the prevention pillar of the Women, Peace, and Security (WPS) agenda, can serve as a powerful tool for anticipating conflict, protecting individual security, and strengthening democratic resilience.

AI implementation within existing security frameworks is largely reactive, focused on operational or postcrisis response rather than prevention, while initiatives that develop early warning indicators or build social cohesion and community resilience are limited in scope and implementation. Using AI as a tool for prevention requires deliberate choices and understanding to rethink not only how AI is used but also the broader security paradigms that determine which risks are prioritized, how tech systems are governed, and whose security is considered actionable.

The WPS agenda provides a proven model for conflict prevention. Applying the WPS model, and the broader humanitarian principles it draws on, to AI governance and policy structures would facilitate more comprehensive and context-sensitive approaches to conflict prevention. Reframing AI from a reactive solution to a preventive instrument across government, civil society, and the private sector offers a critical opportunity to reduce both digital and physical pathways to conflict while advancing more inclusive and sustainable security outcomes.

Policy Recommendations

Current approaches to AI governance and policy globally remain fragmented, largely voluntary, and primarily oriented toward innovation, risk management, or post-harm mitigation rather than prevention. Existing frameworks, including principles advanced by bodies such as the Organization for Economic Cooperation and Development and emerging regulatory efforts within the EU establish important norms around transparency, accountability, and safety. However, they do not systematically address early warning, conflict prevention, or the protection of individual and community security as core objectives.

No comprehensive governance framework integrates gender analysis, conflict-prevention research, and the WPS agenda into the life cycle and application of AI systems for security. This gap between the capabilities of AI and its application in preventing conflict and mitigating risks is increasing. Addressing it requires coordinated action across sectors and issue areas to move beyond reactive governance toward a prevention-focused approach.

For the U.S. Government and Congressional Initiatives

Prevention must be embedded as a core objective of AI use and national security. Departments and agencies must establish and enforce requirements for gender-responsive and conflict-informed risk assessments for AI systems deployed (and being developed) in governance, security, and information environments, using existing laws and strategies (such as the Elie Wiesel Genocide and Atrocities Prevention Act of 2018 and the U.S. Women, Peace and Security Act of 2017), ensuring that risks are identified and mitigated before harm occurs. The WPS Act, Elie Wiesel Act, and other existing policy frameworks provide mechanisms and outline avenues for preventing conflict and promoting social cohesion through elevating and implementing a whole of society approach to developing responsible policies and implementation initiatives.

• AI governance should build upon these structures rather than entirely separate processes. This requires mandating the integration of WPS prevention principles when using AI as a tool for diplomacy, foreign policy engagements, and security planning. Federal funding should be directed toward targeted research that examines how AI shapes early warning indicators, individual security, and democratic resilience.

For Think Tanks, Academic Institutions, and Nonprofits

Advance applied, interdisciplinary research that directly links the developing AI governance structures with WPS and conflict prevention outcomes to bridge the persistent gap between technical development and policy implementation.

• Organizations and leaders should translate technical risks into actionable tools that policymakers can implement, moving beyond abstract analysis to operational guidance. The Institute of Strategic Dialogue’s (ISD) work on misogynistic radicalization pathways offers a strong example of this dynamic.
• By going beyond the absence of gender-disaggregated data on TFGBV in some platforms’ algorithms, ISD delineated specific policy asks, including standardized transparency reporting requirements, survivor centered standards, and mandated intersectional analysis in AI risk assessments.
• In addition to analysis and proposed tools, think tanks, academic institutions, and nonprofits can serve as conveners, bringing together tech leaders, security practitioners, and WPS experts to share insights and coordinate responses. Partnerships are key in this effort.

For the Private Sector

Prevention must be integrated into the full life cycle of AI systems. This includes embedding gender analysis and conflict-prevention metrics into design, testing, and deployment processes rather than treating them as secondary considerations.

• Companies must take an active role in identifying and mitigating AI-enabled harms, including deepfakes, coordinated harassment, and mis- and disinformation that disproportionately target women and marginalized populations.
• Invest in education initiatives and improve efforts for AI literacy for policymaking and public resilience. Ensure education highlights human-led critical thinking and expert-led implementation.
• Institutionalizing transparency and accountability mechanisms is essential to ensure alignment with democratic values and human security, particularly as AI systems scale and shape public discourse.

Reframe AI as a Preventive Security Tool

• Digital gender-based violence and targeted attacks must be recognized as core national and international security concerns.
• Investments in AI literacy are necessary to strengthen both policymaking and public resilience, while reinforcing that AI supports, rather than replaces, human judgment and expertise.
• Cross-sector collaboration must be institutionalized as a standard component of AI governance, ensuring that prevention is embedded in both policy design and implementation.