Always On: A Real-Time, Integrated Intelligence Platform for Australia's Emergency Services

■ During any major event, the information already exists: Bureau of Meteorology feeds, satellites, geospatial data, dispatch systems, workforce rosters and community reports. Too often it sits in separate systems. ■ The biggest step change is integrating what we already have into one real-time, always-on intelligence platform: sense, fuse, predict, recommend, deploy, protect, learn.

■ AI recommends; people decide. Accountability stays with trained incident management personnel. ■ Underneath the six data domains sits asset and readiness data: fleets, aircraft, facilities and critical stock.

Operational capability is people, assets, facilities and supplies, ready together. ■ A platform this critical must be secure by design: zero trust, sovereign data, resilient infrastructure and tested degraded-mode operations. ■ This edition adds each data domain in depth, how the platform could be built, governance, a threat model, degraded modes in detail, success criteria for each phase and questions for boards.

Part 3 of my series on AI, climate extremes and the future of Australian emergency services In Part 1 I looked at the operational, safety, infrastructure and technology challenges facing Australian fire services and their partners. In Part 2 I looked at global advances. This article brings those threads together around one idea.

The biggest step change available to Australian emergency services is not a single new technology. It

is integrating the systems we already have into one real-time, always-on intelligence platform that predicts, deploys and protects, securely. A satellite above Earth. Space-based sensing is one of six data domains an integrated platform must fuse.

Source: Photo by Mara F, Unsplash The problem: information everywhere, insight nowhere During any major event, an enormous amount of information already exists: ■ The Bureau of Meteorology is issuing forecasts, warnings, radar and lightning data. ■ Satellites such as Himawari-9, and hotspot services like Digital Earth Australia Hotspots, are detecting heat signatures. ■ Geospatial systems hold fuel loads, terrain, flood models, road networks, critical infrastructure and property data.

■ Computer-aided dispatch (CAD) systems in fire, Police, Ambulance and SES know where incidents are and where resources are committed. ■ Workforce and volunteer systems know who is qualified, available, fatigued or has been deployed for ten days straight. ■ Social media and community reports are often the first sign of a new ignition, a road washing out or someone trapped by rising water.

Yet in too many control centres these sources still sit in separate systems, on separate screens, owned by separate agencies. Analysts and incident controllers do the integration in their heads, under pressure, often at 3am. Australia has made real progress.

Victoria's Emergency Management Common Operating Picture (EM-COP) and other state common operating pictures are good examples. The next step is moving from a shared map to a shared, AI-enabled, secure intelligence engine that runs continuously before, during and after events. The cost of fragmentation Fragmentation has real costs, even when everyone is working hard:

■ Delay. Information passed by phone or radio and then re-entered into another system can take many minutes, or longer, to reach the person who needs it. ■ Error.

Every manual transfer is a chance to mistype a location, miss a detail or lose a message. ■ Conflict. Agencies working from different pictures may make inconsistent decisions, such as different evacuation advice for the same area.

■ Overload. Analysts and controllers spend scarce time assembling the picture instead of thinking about what it means. ■ Lost learning.

When information is scattered, it is hard to reconstruct what was known and when, which makes after-action reviews less useful. None of these is a failure of people. They are the predictable result of systems that were never designed to work together.

The vision: one platform across six data domains 1 Weather and climate: forecasts, radar, lightning, wind changes, rainfall, river heights and fire danger ratings, with longer-range outlooks from the Australian Climate Service. AI highlights critical moments such as a wind change arriving three hours early. 2 Geospatial: terrain, fuels, flood models, roads, bridges, power and telecommunications, hospitals, aged care facilities, schools and known vulnerable residents.

Digital twins let agencies test "what if" scenarios before they happen. 3 Satellite and aerial: thermal hotspots, burn scars, flood extent, radar imagery and live aircraft and drone feeds. 4 Computer-aided dispatch: real-time incident and resource data from all agencies, linked rather than siloed.

5 Workforce and volunteers: qualifications, availability, rosters, fatigue indicators and wellbeing flags. This is the human layer. 6 Social and community intelligence: public posts, images, community reports and Triple Zero call patterns, filtered, verified and geolocated, with misinformation flagged before it spreads.

Six data domains, one platform, one continuous cycle. AI recommends; people decide. Source: Darren Covington The six domains in depth Weather and climate Sources: Bureau of Meteorology forecasts, observations, radar, lightning and warnings; fire danger ratings; river and rainfall gauges; longer-range outlooks.

What AI adds: nowcasting of storms and wind changes, downscaling of forecasts to local areas, and automatic flagging of critical changes against plans. Challenges: making sure local agency data and Bureau data are combined consistently, and that forecast uncertainty is shown clearly. Geospatial Sources: terrain, vegetation and fuel loads, buildings, roads, bridges, utilities, critical facilities, flood models and population data.

What AI adds: automated updating of fuel and building data from imagery, and scenario modelling through digital twins. Challenges: keeping data current across many custodians, and protecting sensitive layers such as vulnerable residents. Satellite and aerial Sources: satellite hotspots and imagery, radar for flood mapping through cloud, aircraft line scans, drone imagery and live video.

What AI adds: automatic detection of fire edges, burn scars, flood extent and damage, turning imagery into maps within minutes.

Challenges: volume of data, latency between capture and use, and verifying automated outputs. Computer-aided dispatch Sources: incident and resource data from fire, Police, Ambulance and SES dispatch systems. What AI adds: linking related incidents across agencies, predicting demand and recommending resource allocation.

Challenges: different systems and data standards in each agency, and the sensitivity of some incident information. Workforce and volunteers Sources: rosters, availability, qualifications, deployment history, fatigue indicators and wellbeing flags. What AI adds: fatigue-aware deployment recommendations and skills matching across agencies.

Challenges: privacy, trust and consent, and keeping volunteer data accurate without adding to volunteers' workload. Social and community intelligence Sources: social media, community reports, app submissions, photos and Triple Zero call patterns. What AI adds: filtering, geolocating and verifying reports, clustering them into incidents, and spotting misinformation early.

Challenges: separating signal from noise, protecting privacy, and avoiding over-reliance on communities that post more online. Underneath the six domains: asset and readiness data Knowing where the hazard is only helps if the capability to respond is actually there. Every recommendation the platform makes, whether to stage a strike team, send an aircraft or open an evacuation centre, assumes the assets exist and are ready.

Underneath the six domains therefore sits a readiness layer that brings together: ■ Ground fleet readiness. The serviceability of every tanker, pumper, rescue appliance and support vehicle, from telematics and maintenance systems, including predicted faults and planned servicing. ■ Aviation readiness, fixed and rotary wing.

Which aircraft are serviceable, where they are based, their maintenance windows, crew duty hours and contract availability, including interstate and national arrangements. ■ Facility status. Whether stations, depots, airbases, warehouses and operations centres are operating normally, on backup power, cut off by fire or flood, or at risk.

■ Inventory and supply. Stock levels and locations for critical items such as breathing apparatus, PPE, hose, fuel, retardant, sandbags, food, water and spare parts, with forecast demand for the days ahead. ■ Purchasing and supply chain.

Orders in progress, supplier lead times and risks, so leaders can see which gaps will be filled before the season and which will not. What AI adds to the readiness layer AI can predict faults before they take an appliance off the road, forecast aircraft availability over the coming days, flag facilities most at risk from the forecast event, and project stock levels against likely demand. Most

importantly, it links readiness to risk: when the outlook shows a Catastrophic day in a region where two tankers are due for servicing and the nearest airbase is short of retardant, the platform raises it in time to act. Why this is critical Operational capability is people, assets, facilities and supplies, ready together. An always-on platform that knows everything about the hazard but nothing about the capability to respond will produce recommendations that can't be delivered.

The readiness layer is what turns intelligence into a response that actually arrives. From data to action: the predict-and-deploy loop 1 Sense. Data flows in continuously from all six domains.

2 Fuse. AI correlates the signals. A satellite hotspot, a lightning strike, a social media photo and a Triple Zero call from the same grid square become one verified incident.

3 Predict. Fire spread, flood inundation and storm impact models run automatically over the next 1, 6, 12 and 24 hours. 4 Recommend.

The platform suggests options: pre-position strike teams, stage ambulances, close a road, issue a Watch and Act warning. 5 Deploy. Incident controllers make the decisions.

Approved actions go straight to CAD, rostering, aviation tasking and public warning systems. 6 Protect. The platform monitors crews and conditions and raises alerts the moment risk escalates.

7 Learn. After every event, outcomes feed back into the models. The guiding principle doesn't change: AI recommends, people decide.

Accountability stays with trained incident management personnel working within AIIMS. Where AI fits in the loop, and where it doesn't AI is most useful in the fuse and predict stages, where it can process large volumes of data quickly and spot patterns people might miss. It can support the recommend stage by generating options and estimating their consequences.

It should not make the deploy decision, which belongs to accountable people. In the protect stage, it can monitor and alert, but people decide what to do. And in the learn stage, it can help analyse what happened, but the conclusions and changes are human responsibilities.

Keeping the loop honest An always-on loop can create its own risks. Predictions can become self-fulfilling if resources are always sent where the model expects trouble and never where it doesn't. Recommendations can drift as conditions change.

And users can come to accept the loop's outputs without question. Safeguards include regular comparison of predictions with outcomes, deliberate checks of areas the model rates as low risk, clear records of when recommendations were accepted or overridden and why, and periodic independent review of the whole loop, not just individual models. The frontline edge: the platform in the hands of crews A platform that only exists in the State Control Centre leaves the people making the fastest, highest-stakes decisions without it.

The final step is to put the same intelligence in the hands of frontline volunteers and staff, through integrated, fit-for-purpose applications connected over mobile networks and satellite.

Real-time decisions where the work is done: integrated frontline apps over satellite. Source: Darren Covington What frontline applications need to get right: ■ One aligned toolset, not a dozen apps. Tasking and commander's intent, the shared operating picture filtered to the user's role, crew location, escape routes and safety alerts, fire spread and flood predictions, warnings, photo and voice reporting, availability and fatigue hours, and appliance and stock status, all in one integrated experience.

■ Fit for purpose on the fireground. Designed for gloves, glare, smoke and stress, with simple screens, large controls and voice input, and co-designed with the volunteers and staff who use them. ■ Connected anywhere.

Offline-first apps that sync over mobile networks when available, and over low-earth-orbit satellite terminals on appliances and command vehicles, or direct-to-device satellite messaging on phones, when they are not. ■ Radio stays primary for safety-critical voice. Apps add the shared picture and data; they don't replace the radio.

■ Secure by design. Managed devices, multi-factor sign-in, encryption, remote wipe for lost devices, and data held under Australian jurisdiction. Closing the loop from the frontline The frontline edge works in both directions.

Crews receive intent, predictions and alerts, and they send back what only they can see: the fire behaviour in front of them, a road that has just been cut, a resident who needs help, an appliance fault or a stock shortage. Those reports, geolocated and time-stamped, flow straight into the platform and improve the picture for everyone. With this edge in place, the predict-and-deploy loop reaches all the way to the crew on the fireground.

Safety for the community An integrated platform changes how agencies protect the public: ■ Earlier, more targeted warnings. Warnings aligned to the Australian Warning System, sent to the specific streets and catchments at risk through AusAlert cell broadcast, apps, radio and social media, in multiple languages and accessible formats. ■ Proactive evacuation of vulnerable people.

Knowing in advance where aged care residents, hospital patients, people with disability and isolated residents are, so Police, Ambulance and SES can coordinate evacuation before roads are cut. ■ Dynamic evacuation routing. Routes based on live fire spread, flood levels and traffic, updated as conditions change, rather than fixed plans that may no longer be safe.

■ Fighting misinformation. Rapid identification and correction of false information that could put people in danger. Vulnerable people data: getting it right Knowing where vulnerable people are can save lives, but it is among the most sensitive information an agency could hold.

Getting it right requires: ■ A clear legal basis for collecting and sharing the information. ■ Consent where possible, through voluntary registers and partnerships with health, aged care and disability services. ■ Minimum necessary detail: location and level of support needed, not full medical records.

■ Strict access controls, with only authorised people able to see it, and only during relevant events. ■ Audit and accountability for every access. ■ Regular updates, because outdated information can send help to the wrong place.

Safety for staff and volunteers The same platform protects the people we send into harm's way: ■ Live crew tracking against the hazard. Every crew, appliance and volunteer team is shown in real time against the predicted fire front or flood extent, with automatic alerts when a crew's position becomes untenable or an escape route is compromised. ■ Fatigue-aware deployment.

Rostering and deployment recommendations account for hours worked, consecutive shifts, travel time and exposure, so no one is sent out for a fourth consecutive 14-hour day because the system couldn't see it. ■ Skills-matched tasking. Swiftwater technicians, remote area firefighters, paramedics and chainsaw operators are matched to the tasks that need them.

■ Exposure and wellbeing tracking. Smoke, heat and critical incident exposure are recorded over a career, supporting both health monitoring and early intervention. ■ Volunteer protection.

Visibility of volunteer availability across fire, SES and ambulance first responder roles avoids double counting people who serve with more than one agency, and protects their employers and families from unsustainable demands.

A NSW Rural Fire Service volunteer walks a fire trail. Live crew tracking against the hazard protects the people we send into harm's way. Source: Photo by Darren Covington Designing alerts crews will trust Safety alerts only protect crews if crews trust and act on them.

Too many alerts, or alerts that are often wrong, will be ignored. Good design principles include: ■ Few, meaningful alerts, focused on genuine threats to life. ■ Clear actions: each alert says what to do, not just what is happening.

■ Confidence shown: crews know how certain the system is. ■ Confirmation: alerts are acknowledged, and supervisors know when they haven't been. ■ Review: false alarms and missed alerts are reviewed and used to improve the system.

“The data exists. What's missing is integration, and the security to rely on it.” How the platform could be built There are several ways to build an integrated platform, and the choice has major implications for cost, resilience and security. Centralised or federated?

A single central platform is simpler to manage but creates a single point of failure and requires every agency to move its data. A federated approach connects existing systems through common standards and interfaces, leaving data with its owners but making it available to authorised users. Most practical designs combine the two: a shared core for the common operating picture and analytics, connected to agency systems that remain in place.

Key architectural elements: ■ Common data standards and interfaces, so systems can exchange information reliably.

■ An integration layer that collects, validates and routes data between systems. ■ A shared analytical environment where models and AI tools can access fused data. ■ The common operating picture as the main interface for users, with role-based views.

■ Edge capability on command vehicles and at incidents, so key functions continue when connectivity is lost. ■ Distributed, resilient hosting under Australian jurisdiction, with tested failover between locations. ■ Security built into every layer, from identity management to monitoring.

Build, buy or partner? Few agencies will build everything themselves. A sensible approach is to buy or share proven components, build only what is unique to emergency services, and partner across jurisdictions so common capabilities are built once.

The human interface The platform will succeed or fail at the screen. If incident controllers, analysts and crews can't find what they need quickly, the best data and models in the world won't help. Design principles: ■ Role-based views: a State Controller, an ambulance dispatcher and a crew leader need very different information.

■ Exceptions first: highlight what has changed and what needs a decision. ■ Confidence and currency visible: show how reliable and how recent each item is. ■ Mobile and offline: crews and commanders in the field need simple views that work on phones and tablets, including without a network.

■ Accessible and consistent: the same symbols, colours and terms across agencies. ■ Tested with users: designed and refined with the people who will use it, in exercises and real events. The business case A platform of this kind requires significant investment, and it must be justified.

The strongest business cases bring together several kinds of value: ■ Lives and injuries: faster detection, earlier warnings, better evacuation and better crew safety. ■ Avoided losses: smaller fires, earlier flood preparation and faster recovery reduce damage to homes, businesses and infrastructure. ■ Efficiency: less time spent assembling information, fewer duplicated systems and better use of resources across agencies.

■ Resilience: a platform designed to keep working when other systems fail, with tested fallbacks. ■ Learning: better data for after-action reviews, research and future planning. Costs must be counted honestly, including integration with existing systems, ongoing licences, security operations, data management, training and the skilled people needed to run the platform.

Barriers and how to overcome them ■ Legacy systems: connect them through standards rather than replacing everything at once. ■ Data ownership concerns: use a federated design and clear agreements, so agencies keep control of their data. ■ Privacy: build privacy controls in from the start, with independent review.

■ Skills: combine agency staff with partners and shared national capability. ■ Funding across jurisdictions: agree cost-sharing models early, linked to shared benefits. ■ Trust in AI: start with transparent, well-tested tools and build confidence through use.

Cyber security at the core: securing the platform Cyber security at the core: secure by design, not bolted on. Source: Darren Covington A single, integrated, always-on platform would be one of the most valuable and most targeted systems in the country. It would hold a detailed map of community vulnerability, live positions of crews, and the levers that send resources and warnings.

It must be secure by design from the first line of the business case.

Data centre infrastructure. A national intelligence platform must be engineered to be secure, sovereign and resilient. Source: Photo by Winston Chen, Unsplash The security architecture I'd expect to see: ■ Zero trust.

Every user, device, application and data feed is authenticated and authorised continuously, with least-privilege, role-based access across agencies and jurisdictions. ■ Data sovereignty and classification. Data is held under Australian jurisdiction, classified by sensitivity, encrypted at rest and in transit, and handled in line with the Protective Security Policy Framework.

■ Integrity of inputs. Sensor, satellite and social media feeds are verified and monitored for tampering or spoofing before they influence decisions. ■ AI assurance.

Models are tested against manipulation and extreme scenarios, their recommendations are explainable, and every AI-influenced action has a human decision-maker on record. ■ Resilience and degraded modes. The platform is distributed across secure regions with edge capability on the fireground, and every agency has rehearsed manual fallbacks for when it is unavailable.

■ Continuous monitoring. A security operations capability monitors the platform 24/7, integrated with national threat intelligence from the Australian Signals Directorate. ■ Compliance as the floor, not the ceiling.

Essential Eight maturity, Security of Critical Infrastructure obligations and Cyber Security Act 2024 requirements are met as a minimum. A threat model for the platform A platform like this would face a range of threats: ■ Criminal ransomware groups seeking payment by disrupting operations. ■ State-sponsored actors interested in critical infrastructure and sensitive data.

■ Hacktivists seeking attention by defacing or disrupting public services. ■ Insiders, whether malicious or careless, with legitimate access. ■ Supply chain compromise through vendors, software updates or cloud providers.

■ Data manipulation aimed at causing wrong decisions, such as false incident reports or altered sensor data.

Each requires different controls, and together they make the case for defence in depth: multiple layers of protection, so that the failure of one does not compromise the whole platform. Degraded modes in detail Every function of the platform needs a defined degraded mode: ■ Loss of the platform entirely: agencies revert to their own systems and manual coordination, using offline copies of key information. ■ Loss of a data domain, such as satellite feeds: the picture continues with other sources, and users are clearly told what is missing.

■ Loss of connectivity at an incident: edge systems keep working with the last known picture and synchronise when connection returns. ■ Suspected data manipulation: affected feeds are quarantined, and decisions rely on verified sources. Each mode should be documented, trained and exercised, so that staff can move between them calmly under pressure.

Governance of the platform A shared platform needs shared governance: ■ An ownership model agreed between participating agencies and jurisdictions, including who funds, operates and secures it. ■ Data-sharing agreements setting out what is shared, for what purpose and under what controls. ■ A design authority that sets standards and approves changes.

■ An AI assurance function that tests and approves models before operational use. ■ A security authority with the power to require fixes and, if needed, to disconnect systems that put the platform at risk. ■ User and community representation, so frontline staff, volunteers and community voices shape priorities.

Testing and assurance before go-live A platform that people's lives depend on must be tested thoroughly before it is relied on: ■ Functional testing that every feed, model and view works as designed. ■ Load testing at the volumes expected on the worst days, not average days. ■ Failover testing that moves operations between hosting locations without loss.

■ Security testing, including independent penetration testing and red team exercises. ■ AI assurance, including testing models against extreme scenarios and manipulated inputs. ■ User testing in realistic exercises, with incident managers, crews and partner agencies.

■ Parallel running alongside existing systems for at least one high-risk season before they are retired. A day in the life: how it could work Here is an illustrative scenario.

06:00. The Bureau forecasts a Catastrophic fire danger rating, followed by severe thunderstorms on burn scars within 48 hours. The platform flags a compound risk: fire today, flash flooding and debris flows tomorrow.

07:30. Models identify three communities at highest risk. The readiness layer shows a tanker in the area flagged for a pump fault, a helicopter due for scheduled maintenance at midday, and low retardant stock at the nearest airbase.

Servicing is brought forward, a replacement aircraft is requested, and retardant is resupplied. Recommendations go to the State Control Centre: pre-position strike teams, stage ambulances near an aged care facility, put SES flood rescue teams on standby, and prepare warnings. 13:42.

Dry lightning. Within four minutes, satellite detection, an AI camera and two social media posts are fused into one verified ignition. The closest crews, matched for skills and checked for fatigue, are recommended and dispatched through CAD.

14:10. The spread model shows the fire reaching an evacuation road within 90 minutes. Police manage traffic, an Emergency Warning goes out through AusAlert, and Ambulance moves aged care residents early.

15:05. The security operations team detects an attempt to inject false incident reports into the platform. Unverified feeds are automatically quarantined, and commanders continue on verified data.

The attempt is reported to national authorities. 16:25. The wind change arrives 40 minutes early.

The platform detects that a volunteer brigade's escape route is about to be cut, and the crew is alerted and redirected. Next day. Flood and debris flow models activate.

Pre-positioned SES teams are in place, and residents warned hours earlier have already moved. Beyond response: recovery and preparedness The platform's value extends well beyond the response. During recovery, the same data can guide damage assessment, prioritise assistance, track the restoration of power, water, roads and communications, and help communities understand what happened.

During preparedness, it supports planning, exercising, community education and investment decisions. Using the platform all year round also keeps it healthy. Systems used only in emergencies tend to decay: data goes stale, users forget how to use them and faults go unnoticed.

A platform used every day, for planning, training and smaller incidents, is far more likely to work when the big event arrives. Working across borders Major events often affect more than one jurisdiction, and resources move between states and territories, and sometimes countries. The platform should be designed for that from the start: ■ Common standards so neighbouring jurisdictions' platforms can exchange information automatically.

■ Guest access for deployed interstate and international crews, with appropriate controls. ■ A national view of major events and resources, drawing on each jurisdiction's platform. ■ Consistent security requirements, so connections between platforms don't create weak points.

The people who will run it A platform is only as good as the people who run, use and improve it. New and expanded roles will include: ■ Platform operations staff who keep feeds, integrations and services running around the clock. ■ Data stewards responsible for the quality of each domain's data.

■ Intelligence analysts who interpret fused data and models for decision-makers. ■ Model specialists who develop, test and monitor AI tools. ■ Security operations staff who monitor and defend the platform.

■ Trainers who keep users across agencies confident and current. Many of these roles can be shared across agencies or jurisdictions, making specialist skills affordable for smaller services. The same day without the platform Run the same day without an integrated platform, and the differences are stark.

The compound risk is noticed by individual forecasters but not brought together into one assessment. The dry lightning ignition is reported by a member of the public some time later, by which time it has grown. Information about the aged care facility reaches Ambulance by phone after the fire has already threatened the road.

The false incident reports are acted on before anyone realises they were fake. The volunteer brigade learns its escape route has been cut when it sees the fire. And the debris flow warnings go out after the rain has started.

None of these failures would come from lack of effort or skill. They would come from information arriving too late, in pieces, to the wrong people. That is the gap an integrated platform is designed to close.

Ethics and public trust An always-on platform that knows where vulnerable people live, where crews are and what communities are posting online is a powerful tool. It must be used in a way the community would support. That means: ■ Purpose limitation: using the platform only for emergency management, never for unrelated enforcement or commercial purposes.

■ Transparency: publishing what data the platform uses, how it is protected and how AI is governed. ■ Oversight: independent review of privacy, security and AI use, with findings reported publicly. ■ Redress: clear ways for people to ask what information is held about them and to correct it.

■ Community voice: involving community representatives in governance, including First Nations, multicultural and disability organisations. What it takes: the enablers Building an always-on integrated platform depends on more than technology: 1 Common data standards across agencies and jurisdictions, so systems can actually talk to each other. 2 Resilient, always-on infrastructure, with redundant communications, cloud and edge computing, and satellite fallback, so the platform keeps working when the event knocks out local networks.

3 Cyber security by design, as set out above, funded and governed at executive level. 4 Data sovereignty and privacy. Sensitive information about vulnerable people, staff health and volunteers needs strong governance, clear legal authority and community trust.

5 Responsible AI governance. Transparent models, testing against extreme and unprecedented scenarios, human accountability and independent assurance. 6 Workforce capability.

Training incident managers, analysts and frontline crews to use AI-supported insights well, and to know when to challenge them. 7 Co-design with the frontline. Career staff, volunteers, unions and communities shaping how the system works from the start.

8 Asset, maintenance and supply chain data, so the readiness of fleets, aircraft, facilities and critical stock is visible in real time and purchasing decisions are informed by what the data shows. 9 Frontline applications and satellite connectivity, so volunteers and staff can see, decide and report in real time wherever they are. 10 National coordination.

Working with NEMA, AFAC, the Bureau of Meteorology, Geoscience Australia and state agencies, so we build once and share across the country rather than building eight different versions. Data quality: the unglamorous foundation The most sophisticated platform will fail if its data is wrong. Out-of-date volunteer availability sends requests to people who can't respond.

Incorrect appliance status shows capability that doesn't exist. Stale road data sends evacuees towards closed roads. Data quality is unglamorous work, but it is where many integration projects succeed or fail.

Practical steps: ■ Name an owner for each critical data set, responsible for its accuracy. ■ Make updating easy, especially for volunteers, through simple mobile tools and automatic updates where possible. ■ Show data age on the platform, so users can see when information was last confirmed.

■ Check automatically for gaps, duplicates and inconsistencies, and fix them before the season. ■ Learn from events, by reviewing where poor data affected decisions and correcting the cause. A practical roadmap ■ Phase 1: Connect.

Integrate weather, satellite, geospatial and CAD data into existing common operating pictures, agree data standards, and establish the security architecture. ■ Phase 2: Predict. Add AI-driven fire, flood and storm impact modelling and automated incident fusion, with AI assurance and input-integrity controls, plus predictive maintenance and aviation availability forecasting in the readiness layer.

■ Phase 3: Protect. Integrate workforce and volunteer systems, with fatigue-aware deployment and live crew safety monitoring under strict privacy controls, together with real-time inventory and facility status, and roll out the integrated frontline app over satellite and mobile networks.

■ Phase 4: Optimise. Move to a fully cross-agency, cross-jurisdictional national platform, with 24/7 security monitoring and continuous learning after every event. Success criteria for each phase ■ Connect: core data from weather, satellite, geospatial and dispatch systems flows into the common operating picture; data standards are agreed; the security architecture is in place and tested.

■ Predict: automated incident fusion and predictive models are used routinely, with assurance testing completed and input-integrity controls operating. ■ Protect: workforce and volunteer data is integrated under privacy controls; fatigue-aware deployment and crew safety alerts are in operational use and trusted by crews; the frontline app works over satellite where there is no mobile coverage. ■ Optimise: the platform operates across agencies and jurisdictions, with 24/7 security monitoring and continuous learning after every event.

Measuring success ■ Time to awareness for new incidents and significant changes. ■ Time from decision to action, including warnings. ■ Crew safety outcomes, including alerts acted on and near misses.

■ Platform availability, including during major events. ■ Operational capability: availability of appliances, aircraft and critical facilities on high-risk days, and critical stock held against target. ■ Security posture, including Essential Eight maturity and results of independent testing.

■ User trust, measured through surveys of incident managers, crews and partner agencies. Questions for boards and executives ■ How many separate systems do our incident controllers rely on today, and how much time do they spend combining them? ■ Who would own, fund and secure a shared platform across agencies and jurisdictions?

■ Can we see, in real time, which appliances, aircraft, facilities and critical stock are ready to respond? ■ How would we keep operating if the platform were unavailable during a major event? ■ How do we protect vulnerable people's data while using it to keep them safe?

■ What assurance do we require before AI recommendations are used in operations? Starting small The roadmap's four phases describe a destination, but the first step can be modest. An agency could start by bringing Bureau of Meteorology warnings, satellite hotspots and its own dispatch data into a single view for one region during one season, with a dedicated analyst, clear security controls and an honest evaluation at the end.

If that view helps people decide faster and better, it builds the case, the skills and the trust for the next step. If it

doesn't, the lessons are cheap and valuable. Either way, the journey to an always-on platform begins with a single, well-run pilot. Closing thought Climate extremes are now constant, compound and overlapping.

Our emergency services can't afford to piece together the operating picture by hand while the event unfolds, and they can't afford a platform that fails, or is compromised, when it is needed most. An integrated, always-on, secure-by-design platform would let us predict earlier, deploy smarter and, most importantly, bring both the community and our people home safely. The data exists, the technology is maturing and the need is clear.

What's needed now is the leadership and collaboration to bring it together. Glossary ■ Common operating picture: a shared, real-time view of an event that all agencies can see. ■ Defence in depth: multiple layers of security, so the failure of one doesn't compromise the whole system.

■ Edge computing: processing data close to where it is collected, such as on a command vehicle, so functions continue without a network. ■ Federated architecture: connecting existing systems through common standards rather than moving all data into one system. ■ Fusion: combining data from several sources into a single, more reliable picture.

■ Zero trust: a security approach in which no user, device or data feed is trusted by default. Further reading ■ Australian Government, Protective Security Policy Framework. ■ Australian Signals Directorate, Essential Eight Maturity Model.

■ Australian Government, Cyber Security Act 2024. ■ Royal Commission into National Natural Disaster Arrangements, Report (2020). ■ AFAC, Australasian Inter-service Incident Management System (AIIMS).

What would it take to make this a reality in your organisation or jurisdiction? #EmergencyManagement #AI #CyberSecurity #ZeroTrust #CommonOperatingPicture #ClimateResilience #FireServices #SES #PublicSafety #DigitalTwin #Australia #ResponsibleAI Darren Covington writes on intelligence-driven emergency services, climate resilience, technology and cyber security in Australia. Photo credits: Mara F / Unsplash; Darren Covington; Winston Chen / Unsplash.

Unsplash photos are used under the Unsplash Licence. Other photos and graphics © Darren Covington.