Island Networks has been shortlisted as a finalist in the Best Application of AI in Cybersecurity category at the 2026 AI Awards Ireland. For us, the significance of that recognition lies in the problem behind the project.
Working with a global high tech manufacturing organization with more than 70,000 employees across 130 countries, our team set out to address a challenge familiar to large security operations: organizations can have enormous volumes of cybersecurity data security tools, yet still struggle to turn that information into fast, accurate and actionable decisions. The project demonstrates a practical model for AI enabled cybersecurity; one built around existing security investments, specialist human expertise, responsible AI, and better decision making.
For the organization’s security analysts, investigating a potential threat meant navigating multiple security tools and data sources, manually gathering evidence, correlating information, and applying specialist knowledge to understand what had happened.
The problem was not simply detecting threats but establishing context. Large cybersecurity environments generate vast amounts of information about identities, devices, applications, vulnerabilities, cloud resources, and security events. But individual alerts rarely provide everything an analyst needs to make an informed decision. Connecting those pieces manually takes time and depends heavily on experienced specialists who understand both the tools and the underlying security context. Island Networks therefore focused on closing the gap between cybersecurity data and cybersecurity decisions.
Rather than introducing another standalone security product or replacing existing security platforms, Island Networks developed a secure AI driven cybersecurity capability designed to work alongside the organization’s existing environment.
We established a cross functional team bringing together cybersecurity experts, AI specialists, data engineers, and governance stakeholders. Together, the team identified high value cybersecurity use cases and defined success criteria.
The project deliberately started small. A proof-of-concept approach was used to validate feasibility, governance, and user adoption before expanding into additional cybersecurity workflows. Continuous testing and feedback enabled the team to refine capabilities and improve accuracy as the platform developed. This phase approach was important because introducing AI into cybersecurity cannot simply be a technology exercise. Trust, security, governance, and operational usability all need to develop alongside the technology itself.
At the center of the solution is a multi-agent AI architecture. Instead of expecting one AI model to perform every aspect of an investigation, specialist AI agents can perform distinct cybersecurity tasks, including information gathering, data correlation, threat analysis, attack path evaluation, risk assessment, and investigative support. Those agents can then collaborate across more complex security investigations.
Large Language Models enable analysts to interact with the system through natural language, while a multi-agent orchestration framework coordinates the execution of tasks and the exchange of information.
Retrieval Augmented Generation, or RAG, grounds AI responses in authoritative organizational security data. This is particularly important in cybersecurity because an answer needs to be supported by evidence, not just simply sound plausible. Grounding responses in trusted information helps reduce hallucination risk while supporting traceability and explainability.
Security Knowledge Graphs provide another important layer. By representing relationships between assets, identities, vulnerabilities, cloud resources, and other elements of the environment, graph technology helps the AI understand connections and potential attack paths that may be difficult to see when information remains fragmented across separate tools.
Secure API integrations connect the capability to multiple cybersecurity systems, while the cloud native architecture provides a scalable and resilient foundation for enterprise use.
One of the most important elements of the project is also one of the simplest from the user’s perspective: analysts can ask questions in everyday language. Natural-language-to-query translation turns those questions into structured security queries behind the scenes. Instead of requiring every analyst to become an expert in every underlying query language or security platform, the system provides a more accessible way to work with complex security information.
Accessible cybersecurity intelligence makes specialist knowledge and sophisticated security data accessible to a broader group of analysts, while experienced professionals continue to provide judgement, context, and accountability. That distinction is central to Island Networks’ approach to AI.
Human oversight was embedded into the project from the beginning. AI accelerated information-gathering, correlation, analysis, and evidence generation; but analysts retain responsibility for decisions. Outputs are designed to be explainable and supported by evidence and source attribution so that analysts can examine how conclusions were reached. This creates an important separation between AI assisted investigation and human decision making.
The objective isn’t to remove cybersecurity professionals from investigations. It’s to reduce the manual work surrounding those investigations so professionals can spend more of their time applying judgement, assessing risk, and determining the appropriate response.
Achieving that required overcoming challenges that went well beyond deploying an AI model. Security information was fragmented across different platforms, formats, and environments. Bringing those sources together while preserving context and data quality required significant engineering work.
Trust was another major consideration. Security analysts needed confidence that AI generated findings could be examined, validated, and traced back to evidence.
There was also the challenge of capturing tacit knowledge. Experienced analysts often make connections based on expertise developed over many years. Cybersecurity specialists therefore worked directly with the development team to incorporate that knowledge into agent workflows, investigative playbooks and evaluation processes.
Privacy, security, access control, governance, and auditability were incorporated into the solution from the outset. RAG helped ground responses in trusted data, while continuous validation, testing, and user feedback were used to improve accuracy and consistency. These were not peripheral requirements. They shaped the architecture of the platform itself.
One anonymized investigation demonstrated why this broader context matters. An existing security platform identified anomalous user and generated an alert. The detection worked as intended, but analysts would traditionally still have needed to investigate multiple systems to understand the event.
The AI cybersecurity agent orchestrated information from multiple sources to build that context. It identified job application activity involving industry competitors, analyzed relevant application materials and identified sensitive company information being transferred externally. It connected relationships between the user, information, destination and timeline, translated relevant Mandarin language content into English, and generated a consolidated risk assessment and investigative report. The significance was not a final decision AI made; it didn’t replace the analyst’s responsibility. Instead, it turned a security alert into a far richer, evidence supported investigation, giving analysts the context needed to assess the situation and determine the appropriate response.
The difference between detecting something unusual and understanding what it means is one of the most valuable opportunities for AI in cybersecurity.
The result is a capability that reduces manual investigative effort and accelerates time-to-understanding while improving consistency, transparency, and auditability. By automating information-gathering and correlation, the platform allows cybersecurity professionals to spend more time on higher value analysis and response. Natural language interaction broadens access to specialist security intelligence, while evidence-based outputs help analysts understand and validate findings.
The architecture creates a reuseable foundation. Additional specialist agents can be introduced for threat hunting, vulnerability management, incident response, risk assessment, compliance, cyber due diligence, and security operations without rebuilding the core capability for every use case.
Being shortlisted for Best Application of AI in Cybersecurity reflects what we believe responsible enterprise AI should look like in practice.
This project didn’t begin with a search for somewhere to apply AI. It began with a genuine cybersecurity problem. Island Networks combined agentic AI, cybersecurity expertise, existing security technology investments, governance, explainability, and human oversight to address the growing gap between the quantity of security information organizations possess and their ability to translate it into informed action.
That’s the future we see for AI enabled cybersecurity.
As threats, attack surfaces, and security data continue to grow, security teams will need better ways to convert information into understanding. AI can help carry the burden of gathering, correlating, and analyzing that information, while human professionals provide the judgement, accountability, and expertise required to act on it. For Island Networks, being shortlisted for the 2026 AI awards Ireland is recognition of that approach, but it is also a marker of what comes next: building AI capabilities that amplify human expertise and help organizations make better cybersecurity decisions securely, responsibly, and at scale.
Interested in exploring how AI could support your cybersecurity operations? Speak with Island Networks about building practical, governed AI capabilities around your existing security environment.