Embedding secure generative AI in mission-critical public safety applications

This post is co-written with Lawrence Zorio III from Mark43.

Public safety organizations face the challenge of accessing and analyzing vast amounts of data quickly while maintaining strict security protocols. First responders need immediate access to relevant data across multiple systems, while command staff require rapid insights for operational decisions. Mission-critical public safety applications require the highest levels of security and reliability when implementing technology capabilities. Mark43, a public safety technology company, recognized this challenge and embedded generative artificial intelligence (AI) capabilities into their application using Amazon Q Business to transform how law enforcement agencies interact with their mission-critical applications. By embedding advanced AI into their cloud-native platform, Mark43 enables officers to receive instant answers to natural language queries and automated case report summaries, reducing administrative time from minutes to seconds. This solution demonstrates how generative AI can enhance public safety operations while allowing officers to focus more time on serving their communities.

This post shows how Mark43 uses Amazon Q Business to create a secure, generative AI-powered assistant that drives operational efficiency and improves community service. We explain how they embedded Amazon Q Business web experience in their web application with low code, so they could focus on creating a rich AI experience for their customers.

Mark43’s public safety solution built on the AWS Cloud

Mark43 offers a cloud-native Public Safety Platform with powerful computer-aided dispatch (CAD), records management system (RMS), and analytics solutions, positioning agencies at the forefront of public safety technology. These solutions make sure public safety agencies have access to the essential tools and data they need to protect and serve their communities effectively. By using purpose-built Amazon Web Services (AWS) cloud services and modern software architecture, Mark43 delivers an intuitive, user-friendly experience that empowers both frontline personnel and command staff. The solution’s advanced analytical capabilities provide real-time insights to support data-driven decision making and enhance operational efficiency. Mark43 has built a robust and resilient microservices architecture using a combination of serverless technologies, such as AWS Lambda, AWS Fargate, and Amazon Elastic Compute Cloud (Amazon EC2). They use event-driven architectures, real-time processing, and purpose-built AWS services for hosting data and running analytics. This, combined with integrated AI capabilities, positions Mark43 to drive innovation in the industry. With its Open API architecture built on AWS and 100+ integrations, Mark43 connects to the applications and data sources agencies rely on for unmatched insights, situational awareness and decision support. This modern data foundation built on AWS allows agencies to leverage the latest technologies and AI models, keeping pace with the evolving technology landscape.

Opportunity for innovation with generative AI

Agency stakeholders have mission-critical roles that demand significant time and administrative interactions with core solutions. With a cloud native Computer Aided Dispatch (CAD) and Records Management System (RMS), Mark43 was able to bring the same modern solutions that have long infiltrated other industries to make police forces more efficient, replacing legacy systems. Now, Mark43 values the opportunity to leverage AI to support the next evolution of innovative technology to drive efficiencies, enhance situational awareness, and support better public safety outcomes.

Leading agencies are embracing AI by setting high standards for data integrity and security, implementing a central strategy to prevent unauthorized use of consumer AI tools, and ensuring a human-in-the-loop approach. Meanwhile, value-add AI tools should seamlessly integrate with existing workflows and applications to prevent sprawl to yet more tools adding unwanted complexity. Mark43 and AWS worked backwards from these requirements to bring secure, easy-to-use, and valuable AI to public safety.

AWS collaborated with Mark43 to embed a frictionless AI assistant directly into their core products, CAD and RMS, for first responders and command staff. Together, we harnessed the power of AI into a secure, familiar, existing workflow with a low barrier to entry for adoption across the user base. The assistant enables first responders to search information, receive summaries, and complete tasks based on their authorized data access within Mark43’s systems, reducing the time needed to capture high value insights.

In just a few weeks, Mark43 deployed an Amazon Q Business application, integrated their data sources using Amazon Q Business built-in data connectors, embedded the Amazon Q Business application into their native app, tested and tuned responses to prompts, and completed a successful beta version of the assistant with their to end users. Figure 1 depicts the overall architecture of Mark43’s application using Amazon Q Business.

Mark43’s solution uses the Amazon Q Business built-in data connectors to unite information from various enterprise applications, document repositories, chat applications, and knowledge management systems. The implementation draws data from objects stored in Amazon Simple Storage Service (Amazon S3) in addition to structured records stored in Amazon Relational Database Service (Amazon RDS). Amazon Q Business automatically uses the data from these sources as context to answer prompts from users of the AI assistant without requiring Mark43 to build and maintain a retrieval augmented generation (RAG) pipeline.

Amazon Q Business provides a chat interface web experience with a web address hosted by AWS. To embed the Amazon Q Business web experience in Mark43’s web application, Mark43 first allowlisted their web application domain using the Amazon Q Business console. Then, Mark43 added an inline frame (iframe) HTML component to their web application with the src attribute set to the web address of the Amazon Q Business web experience. For example,