{"id":3822,"date":"2026-05-12T15:26:01","date_gmt":"2026-05-12T15:26:01","guid":{"rendered":"https:\/\/www.onvif.org\/blog\/?p=3822"},"modified":"2026-05-12T15:55:41","modified_gmt":"2026-05-12T15:55:41","slug":"the-ai-interoperability-gap-no-one-is-talking-about","status":"publish","type":"post","link":"https:\/\/www.onvif.org\/blog\/2026\/05\/12\/the-ai-interoperability-gap-no-one-is-talking-about\/","title":{"rendered":"The AI Interoperability Gap No One Is Talking About"},"content":{"rendered":"\n

AI is changing how physical security systems work, and how they work together. As this shift accelerates, the industry is starting to see that AI interoperability\u00a0isn\u2019t\u00a0just about moving\u00a0data anymore, but about agreeing on what that data\u00a0actually means. To explore this, ONVIF has formed the AI Working Group. We spoke with\u00a0Peter Damm of Milestone Systems<\/strong>, and chair of the group, about why this work matters and who should be involved.\u00a0<\/p>\n\n\n

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Q: ONVIF has always focused on protocol and connectivity standards. Why expand into AI? <\/strong><\/p>\n\n\n\n

Peter Damm: <\/strong>Connectivity alone isn\u2019t enough anymore. As an industry, we\u2019ve become very good at moving data between systems. But exchanging data and genuinely understanding what another system is communicating are two very different things. And once you bring AI into the picture, that gap really starts to show. <\/p>\n\n\n\n

AI-enabled systems rely on data that is clear, structured, and consistent across system boundaries \u2014 whether you\u2019re talking about natural-language investigation, cross-system correlation, or more autonomous workflows. If the metadata is vague, labeled differently from one system to the next, or missing context, the outcome becomes harder to trust and even harder to explain. <\/p>\n\n\n\n

Within the AI Working Group, members have a space to explore these new challenges together and figure out what interoperability needs to look like next. The goal of ONVIF here is not to define AI models or pick algorithms. What we are focused on is making sure AI-based systems can work together in a way that is safe, transparent, and predictable. That means establishing shared, testable, machine-readable meaning \u2014 things like common semantic models, clear provenance and trust attributes, and interfaces that let systems expose information in ways AI can reason over, and humans can still understand. <\/p>\n\n\n\n

Q: What does this mean in practice for the industry? <\/strong><\/p>\n\n\n\n

Peter Damm:<\/strong> 
We\u2019re clearly moving toward what you might call multi-system intelligence. Video, access control, alarms, sensors, and building systems are no longer isolated. They\u2019re increasingly feeding into a shared operational environment. But to make that work, transport-level interoperability isn\u2019t enough. <\/p>\n\n\n\n

What\u2019s really needed is a shared language, something that different systems, and increasingly AI-driven systems, can reason over in a consistent way, regardless of vendor. Without that foundation, things start to break down in subtle but important ways. Events get correlated incorrectly, and context drops out. Decisions become difficult to explain or validate. In safety and security, those aren\u2019t small problems. <\/p>\n\n\n\n

Some of the use cases pushing this forward include natural-language search and investigation, AI agent-based workflows, and cross-domain context fusion where decisions need to be backed by evidence. A big part of the work in the AI Working Group is to model some important core concepts: observation, inference, and action: what was sensed, what was concluded, and what was triggered. Today, those ideas are often flattened into generic metadata, which causes confusion downstream. <\/p>\n\n\n\n

Provenance is another key piece. Important assertions should carry information about where they came from, when they were made, and, where appropriate, how confident the system is. That\u2019s essential for building systems that are not just interoperable, but also trustworthy and explainable. <\/p>\n\n\n\n

This isn\u2019t just theoretical work. Conformance has always been central to ONVIF. The long-term aim is interoperability that can be profiled, tested, and validated in the same structured way ONVIF has always done it. <\/p>\n\n\n\n

Q: What kinds of companies are you looking for to help shape this work? <\/strong><\/p>\n\n\n\n

Peter Damm:<\/strong> 
We\u2019re really interested in companies that are already running into these challenges in the real world. Organizations that are working across multiple systems and domains and are feeling the limits of today\u2019s fragmented, vendor-specific, and often ambiguous metadata. <\/p>\n\n\n\n

If your product depends on combining information from different systems, this work will probably resonate. The same goes if you\u2019re exploring AI in ways that need to be reliable and explainable \u2014 not just clever demos, but systems you can actually trust and deploy at scale. <\/p>\n\n\n\n

In practical terms, that includes several types of companies: <\/p>\n\n\n\n