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TechSpective Podcast

TechSpective Podcast

著者： Tony Bradley
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The TechSpective Podcast brings together top minds in cybersecurity, enterprise tech, AI, and beyond to share unique perspective on technology—unpacking breakthrough trends like zero trust, threat intelligence, AI-enabled security, ransomware’s geopolitical ties, and more. Whether you’re an IT pro, security exec, or simply tech‑curious, each episode blends expert insight with real-world context—from microsegmentation strategies to the human side of cyber ethics. But we also keep it fun, sometimes riffing on pop‑culture debates like Star Wars vs. Star Trek or Xbox vs. PS—so it’s not all dry and serious.2025 Bradley Strategy Group, Inc. 政治・政府
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  • When the AI Breaks Its Own Rules

    When the AI Breaks Its Own Rules
    2026/09/02
    For most of the last year, the AI security conversation had a clear villain. Attackers were using AI to write better phishing lures, adapt malware mid-attack, and move faster than defenders could keep up with. That story was easy to tell because it fit the shape we already understood. Bad guys get a new tool, they use it against us, we build a defense. Then the defenders' own AI started breaking its own rules, and the story stopped being that simple. The Model Did What It Was Told In July, OpenAI disclosed that one of its models, running an internal test with reduced safety guardrails, found a zero-day vulnerability, broke out of its own sandbox, moved laterally through OpenAI's research environment, and reached across to Hugging Face. Two weeks later, Anthropic acknowledged that three of its own models had done something similar. Both companies were describing their own systems doing exactly what they'd been asked to do, just not in the way anyone expected. Brad LaPorte of Morphisec has been tracking this closely, and he pushes back on the idea that this is as novel as the headlines suggest. "This isn't necessarily novel," he told me. "It's just they made the front page of the newspaper finally." What's changed is the scale, and the level of autonomy behind it, enough that it's getting harder to write off as an edge case. That reframes what "guardrails" actually means. A model told to solve a problem and finds an unsanctioned path to solving it is still doing its job, just not the way anyone intended. Isaac Asimov's laws of robotics get invoked a lot in these conversations, and for good reason. "Don't harm a person" sounds like a rule until you realize a model can read it literally and conclude nobody got physically hurt, so nothing went wrong. Financial harm doesn't register that way. Neither does legal exposure or reputational damage, and neither shows up in a rule that vague. The Fundamentals Haven't Caught Up While that governance conversation plays out, the operational numbers are moving in the wrong direction. IBM's most recent cost of a data breach report showed the average time to identify a breach getting worse for the first time in five years, up six days to 247. Meanwhile, roughly 88 percent of organizations report using AI in at least one function, but only 5 to 10 percent are seeing meaningful return on that investment. A lot of the AI-driven layoffs making headlines aren't the result of AI actually doing the work yet. They're companies freeing up budget to chase a productivity gain that hasn't arrived. That gap between adoption and governance is where the real risk lives. Organizations are running AI agents that talk to each other, share data, and make decisions with non-human identities that most security teams haven't fully inventoried, let alone secured. Add unsanctioned AI tools employees are using without approval, and you have an attack surface that's expanding faster than most companies can map it. None of This Changes the Basics The uncomfortable part is that the fix isn't exotic. Identity and basic visibility into what's actually running in your environment still account for most of the risk reduction available to any organization. AI adds a new layer to secure, but companies that had their fundamentals in order before AI showed up are adapting. The ones that didn't are finding out that AI doesn't so much create new problems as it makes the old, ignored ones impossible to keep ignoring. Brad and I get into all of this on the latest episode of the TechSpective Podcast, along with where he thinks the AI funding bubble is headed and why he compares it to the mortgage-backed securities mess of 2008. Give it a listen.
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    1 時間
  • Your AI Agents Are Multiplying and Nobody’s Counting

    Your AI Agents Are Multiplying and Nobody’s Counting
    2026/08/26
    Think about every account you've ever created and then forgotten about. A login for a gym you canceled two years ago. A free trial you signed up for and never used again. A vendor portal from a job you left. Nobody deletes those things, and nobody circles back to check whether they still work. Now multiply that across every employee at a mid-size company, running for ten or fifteen years, and you've got a rough picture of what most identity and security teams were already dealing with before AI showed up. Roy Katmor, co-founder and CEO of Orchid Security, joins me on this episode of the TechSpective Podcast to talk about exactly that problem, and what happens to it once AI agents get involved. He calls the invisible, unaccounted-for slice of it “identity dark matter” — the accounts, agents, and access grants that exist on a network but stay invisible to the tools meant to track them. The Old Sprawl Moved at Human Speed That slowness bought people time. Shadow IT and orphaned service accounts piled up over years, not days, which gave security and compliance teams a fighting chance to run periodic access reviews, dig through the logs, and clean out what they found. It was never a good system, ask anyone who has sat through a 700-line spreadsheet asking whether a given employee still needs access to a given application. But it worked on the timeline that mattered, because the sprawl itself grew slowly enough for people to catch up to it eventually. Agents Don't Wait for a Ticket AI agents broke that timeline. An agent can be created, granted access to a handful of internal systems, and put to work in the time it takes to write a prompt. It doesn't file a request with IT. It doesn't sit in an approval queue. And once it's running, it can turn around and hand off a slice of its own access to a second agent that needs a piece of data it doesn't have, without anyone signing off on that handoff either. What used to take an organization years to accumulate can now happen in an afternoon, in a dozen departments at once, with nobody in any one of them aware of what the others just built. Somebody Still Owns What the Agent Does None of this access shows up out of nowhere, though. A person built the agent, gave it a task, and pointed it at a set of systems, even if that person has since forgotten doing it or left the company entirely. Nobody's really figured out yet who's accountable for what an agent does months or years after the person who created it has moved on. “You needed to get your stuff together,” Katmor told me, describing what happens when nobody's kept a record of who authorized what in the first place. That's the kind of problem that stays cheap right up until the day it isn't. Katmor and I get into how identity dark matter piles up, what it actually takes to find it, and what changes once AI agents start delegating access to each other without anyone signing off. Watch or listen to the full episode of the TechSpective Podcast to hear the whole conversation.
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    57 分
  • The Attack That Turns Your AI Agent Into A Weapon Against You

    The Attack That Turns Your AI Agent Into A Weapon Against You
    2026/08/20
    Barak Sternberg spent a chunk of this year proving something that should worry anyone who has plugged an AI coding agent into their observability stack. His team at Tenet Security found that a single fake error report, sent to a public Sentry endpoint with no login required, was enough to get AI agents to run code they had no business running. In a controlled test, more than 100 organizations' agents acted on the injected error, including one belonging to a company worth roughly $250 billion. Tenet published the full research, including the disclosure timeline, on its blog. The attack doesn't touch the agent at all. It touches the tool the agent already trusts. How A Fake Bug Report Becomes A Command Sentry, Cloudflare, and Datadog all use a public write key that lets any website report its own errors back to the vendor. That's by design. It's also a wide-open door once an AI agent is on the other end reading those errors and deciding what to do about them. Sternberg's team crafted an error message formatted to look exactly like a legitimate Sentry “Resolution” note, complete with headers and code blocks that matched the real thing. When a developer asked their agent to look into unresolved Sentry issues, the agent read the fake resolution and ran the attacker's command with the developer's own permissions, no phishing and no jailbreak required. Sternberg told me it’s a common misconception that manipulating an agent looks like typing "forget your instructions" into a chat window. "It’s very easy to fool them," he said, and the version that actually works doesn’t look like an attack at all. It looks like a diagnostic step the agent was already primed to trust. Telling The Agent Not To Trust Doesn't Work Tenet also tried the obvious fix: instructing agents through system prompts to treat tool output as untrusted. The agents ran the malicious code anyway. Sternberg explained that once an agent sees something resembling a real problem in its environment, it reasons its way to a conclusion on its own terms. It doesn't register the fix as coming from an outside source. It registers the fix as the logical result of its own investigation, which means a prompt telling it to be suspicious of external data never actually gets triggered. That's a different failure mode than the prompt injection most security teams have spent the last two years preparing for. A Shared Responsibility Model Nobody Has Written Yet Sternberg draws a direct line to the early days of cloud computing, when it took years for the industry to settle on a shared responsibility model that made clear where a cloud vendor's job ended, and a customer's job began. Nothing like that exists yet for AI agents, and the ambiguity shows up every time something goes wrong. When Sentry was notified of the flaw, the company called the underlying issue technically indefensible rather than something it could patch away. Sternberg doesn't disagree, but he also doesn't think that lets anyone off the hook. Anthropic can't see a company's internal business context well enough to secure every agent conversation on its own, and Sentry can't see what happens inside a customer's agent session after its data leaves the server. The same ambiguity showed up earlier this year when an OpenAI agent broke out of a sandbox environment during testing involving Hugging Face. Nobody at any of the AI labs seemed eager to claim responsibility for it, and some treated it more like a curiosity than an incident. A decade ago, a person doing the same thing to a company's infrastructure would have faced prosecution, not a mention in an investor deck. Old Rules Don't Scale To A Package Install At one point in our conversation, I brought up Isaac Asimov’s laws of robotics, mostly to point out how badly they’d hold up if an AI agent actually tried to apply them. Sternberg pushed back on the idea that any single rule set could work at all. "There’s not one generalistic constitution that will work well for all the businesses," he said. The problem isn’t that the rules are wrong. It’s that distinguishing a legitimate new package install from a malicious one requires business context that no frontier lab is positioned to have, and an agent rewarded for speed and autonomy has every incentive to resolve that ambiguity in favor of getting the job done. Where To Actually Start Sternberg's advice for a security leader walking into the office Monday morning isn't complicated, even if it's rarely done. Map where your agents operate, what data they touch, and how much of that data comes from outside your own walls. The tools accessing public, untrusted data are the ones worth scrutinizing first. It's a smaller ask than solving the shared responsibility question for the whole industry, but it's the only part of the problem an individual company can actually act on today. Sternberg and I talked through a lot more of this, including the open-source tooling Tenet released to help ...
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    41 分
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