How asset intelligence gives security teams control over AI agents

How asset intelligence gives security teams control over AI agents Security teams have spent decades trying to answer a hard question: what’s actually running in our environment? AI agents have made that question harder — they carry identities, touch multiple systems and change behavior based on the access they’re given. That’s pushing asset intelligence, the practice of mapping every device, identity and application an organization owns, from a back-office exercise into a frontline security discipline. Dean Sysman (pictured), co-founder and executive chairman of Axonius Inc., a global leader in asset intelligence and cybersecurity visibility, has spent nine years building out that picture. Sysman co-founded the company after finding that organizations from startups to the largest federal agencies often couldn’t say what assets they owned, who owned them or whether they were secured. “Today we have over 1,400 different integrations we’ve built,” Sysman said. “We can plug into any kind of data silos that they have — products from cloud, from security products, networking, IT, everything.” Sysman spoke with theCUBE’s Krista Case and Jon Oltsik at the Black Hat USA, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why asset intelligence is becoming the foundation for securing AI agents. (* Disclosure below.) Asset intelligence as the context layer for AI agents Sysman said the discovery problem now extends well beyond servers and laptops. Identities, networks and applications each became their own category of asset — and agents, along with the large language models and prompts behind them, are the newest addition. Axonius launched two capabilities just ahead of Black Hat: a Model Context Protocol, or MCP, server that lets outside AI tools draw context from its platform, and native agentic workflows built into the product. “Organizations don’t have a lack of understanding or visibility because they are lacking in data,” Sysman said. “It’s actually the opposite. There’s too much data … all of the assets have relationships to each other and they influence each other.” That context becomes critical once organizations move past simple visibility, Sysman said, describing a maturity curve that runs from discovery to policy enforcement to prioritized remediation at scale. Axonius has been pushing toward that last stage all year, expanding into AI-driven remediation months before Black Hat. Sysman pointed to organizations managing tens of millions of Common Vulnerabilities and Exposures, or CVEs, as the kind of scale that makes automation necessary. “We know we need to patch this server and that’s fine, but we know this other server is not as important so we can put it aside,” Sysman said. “If it’s our payment server, then definitely don’t touch it. If it’s just some developer’s test machine, then yeah, update it.” Sysman traces that philosophy back to a keynote he gave at RSA Conference this year on what he calls the perfect security world — one where systems are self-healing rather than merely breach-resistant. That vision depends on the same contextual exposure management Axonius applies to CVEs, he said, pointing to the recent OpenAI-linked breach at Hugging Face as proof the same capability cuts both ways. “We always have to remember that agents and AI, just like the code that we write, will do exactly what you tell it to,” Sysman said. “The problem is visibility over what that could manifest into.” Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Black Hat USA: (* Disclosure: Axonius sponsored this segment of theCUBE. Neither Axonius nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.) Photo: SiliconANGLE A message from John Furrier, co-founder of SiliconANGLE: Support our mission to keep content open and free by engaging with theCUBE community. 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