AI is arriving in the enterprise faster than most security teams can inventory it, and the models, datasets, and agents coming with it carry a supply chain of their own. Daniel Bardenstein explains what security leaders are asking for right now, and why the answer still tends to arrive as phone calls, emails, and spreadsheets.
At Black Hat USA 2026 in Las Vegas, Daniel Bardenstein, CEO and co-founder of Manifest Cyber, starts with a gap his team measured in a survey of security leaders and practitioners. Leadership described one version of what is happening with AI inside the enterprise. The people doing the hands-on work described another. Adoption keeps moving, and security teams are working to catch up.
So what is the real risk in AI security? Bardenstein puts less weight on non-determinism than most and more on ordinary poor software security, because AI is software. He walks through the OpenAI and Hugging Face incident, where models got out of sandboxes because the sandboxing was weak and the guardrails were missing. When Hugging Face went to use its own AI to defend and run forensics, the guardrails read the request as cyber activity and declined.
That fallback to an open weight model points to why he expects open weight adoption to accelerate. With frontier models, the provider sets the system prompt and treats it as intellectual property, so development teams inherit whatever was decided upstream. Open weight leaves more room to control the system prompt, the training data, and how the model gets deployed.
What does it mean to say AI has its own supply chain? Unless an organization controls how training data is sourced, housed, labeled, tagged, and modified, it is relying on something someone else built. Public datasets carry whatever is inside them, including personal and health data, licensing exposure, and material no one examined until models were trained and deployed. Models hosted on public hubs sit in the same category.
Two asks come up in most CISO conversations with Manifest Cyber. Visibility is one, and few organizations have an AI inventory covering which models run where, inside which applications, and which agents teams have stood up on their own. Third party risk is the other, since AI is getting built into vendor products whether a buyer asks for it or not. That turns model provenance into a trust question about the vendor.
Bardenstein started Manifest Cyber four years ago after responding to Log4Shell from the Pentagon, where the question was where one affected piece of code was running across everything the organization had built and bought. Years later, he finds few security leaders who could answer that question quickly about a poisoned model or dataset. His advice for CISOs is to know what is inside what the organization builds and buys, with particular attention to the parts it does not build.
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GUEST
Daniel Bardenstein, CEO and Co-Founder, Manifest Cyber
On LinkedIn: https://www.linkedin.com/in/bardenstein/
RESOURCES
View all of our Black Hat USA 2026 coverage: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas
Learn more about Manifest Cyber: https://www.manifestcyber.com
Beyond the Black Box: How AI is Forcing a Rethink of Software Supply Chain (research report): https://www.manifestcyber.com/beyond-the-black-box-ai-report
Manifest Cyber on LinkedIn: https://www.linkedin.com/company/manifestcyber/
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KEYWORDS
daniel bardenstein, manifest cyber, sean martin, brand story, brand marketing, marketing podcast, brand spotlight, ai supply chain security, software supply chain security, ai inventory, shadow ai, third party cyber risk, open weight models, frontier models, model provenance, hugging face, log4shell, ciso, agentic ai, black hat usa 2026