The future of the help-seeking landscape: Using AI to support earlier, safer DFV responses

Imagine a world where domestic and family violence (DFV) is identified before risk escalates to the point of crisis. Where communities are equipped with systems that detect risk and activate coordinated support in real time, across healthcare, banking, education settings, law enforcement, social services, and digital platforms. This vision could become reality within the next five years if we harness the transformative power of AI to reinvent the way society responds to DFV.

Imagine a victim-survivor experiencing coercive control, speaking quietly to an AI assistant through their phone, smartwatch, or car. In mere seconds, this system could activate a carefully orchestrated safety plan and response: releasing emergency funds, arranging childcare, booking transport, securing safe housing, capturing evidence, and alerting specialist workers. Imagine if these systems could also pick up threat signals from escalating perpetrator behaviours, such as digital stalking, financial control, or repeated breaches, enabling earlier intervention before the violence escalates. Imagine a world in which frontline workers would operate with live intelligence, automated workflows, access to supervision and training, and shared case visibility across agencies, allowing decisions to align with the speed of risk rather than the constraints of bureaucracy.

Reinventing DFV prevention and response requires a paradigm shift. Instead of treating it as a welfare issue managed at the margins, DFV must be considered a core public health and safety challenge, requiring continuous, coordinated action supported by robust infrastructure. AI has the potential to play an important role in bringing about this reality.

The inadequacies of current DFV systems are familiar but urgent in their consequences. Survivors often face a labyrinth of disconnected services, forced to retell their traumatic stories over and over again. Different organisations and sectors hold different pieces of relevant information, but these fragments remain siloed, making it difficult to assess the full scope of risks. Interventions often occur too slowly, constrained by outdated, manual processes that fail to keep pace with rapidly escalating harm. Meanwhile, frontline services are stretched to capacity, meaning they need to prioritise only high-risk cases and limiting the scope for innovation or proactive outreach.

In addition to being wildly inefficient, these gaps represent grave risks for individuals whose safety relies on swift, informed action. For survivors navigating coercive control and harm, the systemic inability to connect the dots can amplify their vulnerability at critical moments. These challenges are not insurmountable but will require reimagining DFV response as an interconnected network, rather than a series of separate parts.

This is where AI comes in.

AI offers the tools needed to transform how DFV is recognised, managed, and prevented. Sophisticated technologies are already enhancing decision-making, triage, and workflow automation in sectors like healthcare, financial services, and crisis response. Applied thoughtfully, these same mechanisms could fundamentally reshape DFV responses. The possibilities are extensive.

First, AI could enable real-time aggregation of risk signals across sectors. Patterns such as uncharacteristic banking behaviour, sudden spikes in utility arrears, or changes in school attendance could flag concerns earlier, triggering support before crises escalate. Privacy-preserving approaches, like federated learning, provide avenues for sharing risk indicators responsibly, allowing organisations to train shared models without compromising sensitive personal data.

Second, survivor-centric tools could empower individuals navigating coercion and harm. For example, AI platforms could assist survivors in documenting evidence, identifying patterns of control, and understanding their options within the DFV support system. These tools could help preserve survivors' ownership over their information, while reducing the emotional toll of having to repeatedly articulate their experiences.

Third, frontline workers could leverage AI to streamline administrative processes, enabling them to spend more time on responding to victim-survivors, and supporting behaviour change with people who use violence. Imagine systems that flag urgent cases based on live data, suggest tailored interventions, and enable cross-agency visibility into case progress. Such technologies hold the potential to accelerate critical decisions while reducing inefficiencies.

Finally, AI could provide proactive insights into perpetrator behaviours. By identifying patterns across touchpoints – such as repeated breaches of safety orders or evidence of digital surveillance – AI tools could help prevent situations from escalating into lethal violence. This capacity to detect coercive dynamics across complex environments could lead to interventions rooted in foresight rather than reaction.

Nous recently brought together senior leaders from across government, community organisations, the private sector, and lived-experience-informed perspectives to discuss what can be done now, rather than in some theoretical future state. The aim was to identify a small number of practical opportunities that could be progressed through cross-sector collaboration. The discussion highlighted several areas of practical opportunity for integrating AI into DFV systems. Chief among these were survivor-focused navigation tools and systems to better detect escalating harm.

Survivor-facing tools offer immediate potential. These platforms could help survivors take control of their situation by capturing evidence, organising records, and connecting with support services discreetly. Importantly, these tools must be designed with safety in mind, from ensuring data privacy, to enabling survivors to decide how and when their information is shared. Another benefit lies in the ability of these tools to help survivors recognise patterns of coercive control, giving them clarity about what they are experiencing and guidance on next steps.

Equally promising is the opportunity to detect hidden or escalating harm earlier. Different parts of society –from banks to telcos to utilities – often hold isolated pieces of data indicating risk. By adopting protocols that enable controlled sharing of these signals, services could offer earlier outreach and proactive intervention. For instance, a flagged pattern of unusual financial transactions combined with police incident history might call for preventive action. While longer-term solutions such as federated learning show promise, immediate opportunities exist to strengthen systems that flag and respond to risk in targeted ways.

In the drive to innovate, the unique risks associated with DFV contexts must remain front of mind. Poorly designed AI interventions could expose survivors to greater harm, whether through breaches of privacy or failure to account for nuanced perpetrator tactics. Balancing safety and ethics will require rigorous design principles, informed by survivor perspectives.

Privacy must be at the core of any AI-enabled solution, ensuring survivors retain agency over their data. Tools should reflect survivor-centric design that prioritises usability, discretion, and comfort. Ethical deployment is critical to avoid unintended consequences, such as reinforcing biases or overlooking cultural particularities. Transparency in how AI systems operate, alongside robust accountability structures, will be indispensable in building trust among survivors and practitioners alike.

Other sectors, such as healthcare and finance, have demonstrated how sensitive ecosystems can successfully integrate AI. DFV response systems can adopt similar approaches, provided they reflect the unique stakes involved in safety and harm mitigation.

Reimagining DFV response systems as AI-enabled safety networks will require sustained collaboration across sectors. Building these systems will need coordinated investment in the tools, protocols, and infrastructures that underpin systemic change. Immediate priorities include piloting survivor-centric tools, prototyping privacy-preserving risk detection models, and fostering cross-agency partnerships to integrate support systems more seamlessly.

Design-led actions such as hackathons, collaborative workshops, or live use-case trials could bring together diverse stakeholders to rapidly iterate solutions. These forums would allow for clear pathways, transforming ideas into implementable prototypes with potential for scaling.

Governments, in particular, have a critical role to play in embedding shared protocols and interoperable standards into DFV response frameworks. With public sector leadership, along with partnership from private organisations, DFV could be addressed not as a fragmented specialist issue but as a societal priority demanding continuous, connected infrastructure.

In the next few years, AI-driven systems could allow DFV responses to operate faster, smarter, and more effectively than ever before. By committing to systemic integration, technological innovation, and survivor-first design, Australia has the opportunity to lead globally in how harm is addressed. The transformation may feel ambitious, but the tools and mechanisms already exist to begin building these networks today.

Deployed thoughtfully, AI could and should become a lifeline, helping survivors regain control, empowering frontline workers to act quickly, and ensuring societal institutions collaborate seamlessly to prevent harm. This is a call to action. Lives depend on it.

Get in touch to discuss how you can leverage AI in your organisation.

Connect with Monique Jackson and Simone Schulz on LinkedIn.