From Reaction to Prevention: Harnessing AI for Conflict Resolution
The Women, Peace, and Security (WPS) agenda has long recognized the importance of prevention in conflict resolution. Now, a new approach is emerging that combines WPS principles with artificial intelligence (AI). This fusion aims to shift the focus from reactive measures to proactive ones, anticipating conflicts before they escalate.
A key challenge lies in how AI systems are currently used within security frameworks. These implementations tend to be focused on operational or post-crisis response rather than prevention. Initiatives that develop early warning indicators or build social cohesion and community resilience are limited in scope and implementation.
The WPS agenda provides a proven model for conflict prevention, drawing on humanitarian principles such as the protection of civilians and respect for international law. Applying these principles 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. This requires deliberate choices and understanding about how AI is used and the broader security paradigms that determine which risks are prioritized.
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 establish important norms around transparency, accountability, and safety but 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 this gap requires coordinated action across sectors and issue areas to move beyond reactive governance toward a prevention-focused approach. For instance, 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.
The U.S. Government and Congressional Initiatives can play a crucial role in this effort by embedding prevention as a core objective of AI use and national security. This involves 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 to ensure that risks are identified and mitigated before harm occurs.
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.
Think tanks, academic institutions, and nonprofits can 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.
The Institute of Strategic Dialogue’s (ISD) work on misogynistic radicalization pathways offers a strong example of this dynamic, translating technical risks into specific policy asks such as standardized transparency reporting requirements, survivor-centered standards, and mandated intersectional analysis in AI risk assessments. 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.
The private sector must also take an active role in integrating prevention 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 should invest in education initiatives and improve efforts for AI literacy for policymaking and public resilience.
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.