Presented by Carahsoft
The open source intelligence community finds itself at a defining inflection point. Across conversations in conjunction with the OSINT Tech Expo, a clear consensus emerged from practitioners and industry leaders alike: the field's greatest challenge has fundamentally shifted. For decades, the problem was finding enough information. Today, the problem is making sense of an overwhelming torrent of it. Veteran practitioners described a landscape where social media posts, geospatial signals, dark web data, commercial telemetry, and technical intelligence are all available simultaneously — and where the sheer volume threatens to paralyze rather than empower the analysts charged with acting on it. The phrase "data toxicity" surfaced more than once as a way of describing how too much unfiltered information can be as dangerous as too little.
Artificial intelligence — and increasingly, agentic AI — emerged as both the primary hope and the most complex variable in addressing this challenge. Interviewees were largely optimistic about AI's potential to accelerate analysis and cut through noise, but uniformly cautious about treating large language models as a complete solution. The recurring emphasis on data quality, source credibility, and human judgment underscored a mature, nuanced community grappling with how to integrate powerful new tools without losing the analytical rigor on which mission outcomes depend. Across every conversation, one through-line was constant: the technology is only as good as the data fed into it, and the data is only as good as the trust placed in its sources.
What makes this possible at scale, Fuster explained, is the application of AI and data science to what had previously been an unmanageable volume of raw, messy information. SpyCloud is now able to render more than 94% of its collected passwords in plain text, dramatically improving their utility for credential protection. The broader implication, he suggested, is a meaningful shift in the tempo of the defender-versus-attacker dynamic: by intercepting and converting stolen data before it can be weaponized, organizations are beginning to close the gap that has historically favored bad actors, whether criminal groups or nation-state operators.
The antidote, in his view, lies in the ability to stitch together disparate data types — social media, dark web records, mobile ad identifiers, breach data — that don't naturally align with one another. Each signal in isolation is weak; combined with confidence into a unified identity, it becomes intelligence. Wallach also pointed to the cognitive warfare dimension of OSINT, citing the now-documented case of a Russian troll's single social media post triggering a real-world emergency response in Louisiana as evidence that the same tools used for analysis are being weaponized offensively. The answer, he said, lies in identity resolution at scale, aided by large language models and agentic AI, tempered by the experience and judgment of seasoned analysts.
Jardines traces the discipline's trajectory from a narrow specialty to something that now underpins every other intelligence discipline. OSINT, he noted, is celebrating its 85th year of supporting national security — and the field's current toolkit would have been unimaginable to him during his earlier career managing IC OSINT efforts. Looking ahead, he is optimistic about AI's capacity to free practitioners from the most time-consuming, low-value tasks in the analytical workflow. The workforce of the near future, he predicted, will be one fluent in scripting, prompt engineering, and data science concepts — a shift that represents both a challenge and a significant opportunity for a discipline that has never been more central to the intelligence enterprise.
Borene pointed to two structural developments he sees reshaping how intelligence agencies acquire and use these capabilities. The first is an emerging ODNI acquisition vehicle — structured as an Other Transaction Authority — that would, for the first time, allow all 18 intelligence community departments and agencies to purchase OSINT tools directly through a common framework. The second is the growing imperative for allied and partner sharing: because OSINT products derived entirely from open sources don't require the declassification and downgrading process that burdens traditional intelligence sharing, they represent a far more fluid mechanism for working with foreign defense and law enforcement partners. The goal, as Borene framed it, is straightforward — US government analysts should have access to at least the same open source tools that a Fortune 500 geopolitical analyst can access by logging into a commercial platform.
The alternative Nixon advocates for is a model where industry engages with customers by first understanding their mission requirements, then honestly assessing what can be delivered and at what cost — a collaborative build rather than a procurement transaction. Janes, a 128-year-old company with more than 500 analysts who physically handle military equipment worldwide, positions itself as a peer collector to the intelligence community rather than a vendor. On the question of AI, Nixon was measured: the technology is only as reliable as the data underneath it, and organizations that chase the LLM hype without interrogating their underlying data sources will be disappointed. Trusted data, he emphasized, is the foundational investment that makes everything else work.
Miller described Dataminr's approach as a deliberate departure from reliance on large foundational models, which he noted are broadly capable but not well-suited to the high-specificity demands of intelligence analysis. Instead, the company operates a constellation of 60 purpose-built smaller models, trained on over a decade of intelligence data, that cross-correlate signals before selectively incorporating foundational models where they add value. The goal is to reduce hallucination and avoid the generalization problem that makes broad LLMs unreliable for operational use. What agentic AI offers the analyst, Miller argued, is not a replacement for human judgment but a dramatically faster path to the moment where that judgment can be applied — stripping away hours of noise so the analyst can focus on mapping information to decision.
The workforce dimension of this challenge is one Helfrich returns to with urgency. Despite a decade of conversation about the cybersecurity talent shortage, the gap remains. Technology can help scale what human analysts do, but it cannot replace the need for people who understand the guardrails — who can recognize when a machine-assisted output needs to be interrogated rather than accepted. Helfrich's prescription is investment at the roots: STEM education at the K–12 level, university partnerships, and an industry commitment to building the next generation of practitioners who can work alongside AI rather than simply deferring to it.
Wall's answer is what Accrete calls a "knowledge engine" or "context engine" — a layer that sits beneath the LLM and does the foundational work of entity extraction, normalization, and relationship mapping before any language model is applied. The practical implication is significant: knowing that two references across different datasets refer to the same individual, and understanding the nature of their relationship to other entities, is what separates data from intelligence. Only once that groundwork is complete does the LLM become a genuinely useful tool for rapid summarization and synthesis. The goal, Wall was clear, is not to replace analysts but to radically expand what they can accomplish — because in the current environment, analysts aren't underperforming, they're simply overwhelmed by a volume of information that no human workforce could process unaided.