When a top security researcher sits down to reverse-engineer a sophisticated piece of malware, they're looking at four to six weeks of painstaking work. They pull apart software, trace execution paths, and often backtrack as new information surfaces. SentinelOne is now doing the same work in roughly eight hours using OpenAI Daybreak, and the implications go way beyond speed.
Gregor Stewart, SentinelOne's Chief AI Officer, frames it in terms that should make every defender sit up: "a kind of intelligence that we were never able to afford before." That's the angle worth unpacking here.
The speed story is real but not the whole story
Yes, collapsing six weeks to eight hours is dramatic. That's roughly a 40x speedup if we're counting working hours. But the raw throughput gain isn't the most interesting part.
What matters is the shift in what becomes economically viable. Security teams have always had to triage ruthlessly—you reverse-engineer the most critical threats, the most interesting samples, and you let the rest go uninvestigated. Stewart puts it plainly: "We were only able to reverse very small numbers of pieces of software."
When analysis is expensive, you get sparse sampling of the threat landscape. You understand individual malware families in isolation but miss the connective tissue between them.
Intelligence you couldn't afford
With Daybreak, SentinelOne can suddenly investigate orders of magnitude more samples. That volume unlocks something qualitatively different: the ability to spot relationships across threats that isolated deep dives would never reveal.
Stewart describes finding shared components between apparently unrelated malware samples—a lead that might prove crucial during a customer investigation. Or mapping out how threat actor tooling evolves over time, because you can finally afford to analyze enough samples to see the patterns.
"Faster reverse engineering enables us to map a much larger part of the malware landscape and keep pace with how it changes. That gives our researchers a clearer picture of how threats are connected and how those relationships evolve."
This is graph thinking applied to malware analysis. Each additional sample you can afford to reverse becomes another node. The value doesn't scale linearly—it scales with the number of potential connections.
Human accountability as the non-negotiable constraint
Stewart is careful to draw a line around what remains human work. "The only thing that can't be automated is accountability," he says. Architectural decisions, changes that fundamentally alter how software behaves—those still need human eyes.
Point fixes or changes that don't touch core architecture? Those might be automation candidates. It's a pragmatic division of labor that acknowledges model capabilities while respecting the stakes.
The framing here matters. SentinelOne isn't positioning this as "AI replaces security researchers." It's "AI handles the grunt work of reverse engineering so researchers can focus on higher-order pattern recognition and decision-making."
That's probably the right mental model, though I suspect the boundary between "routine fix" and "architectural decision" will get fuzzy as models improve. For now, keeping humans in the accountability loop seems like the obviously correct stance.
Democratizing expertise that was bottlenecked
One of the more interesting second-order effects Stewart mentions: access to OpenAI's cyber capabilities is letting more people at SentinelOne take on security work that previously required scarce specialists.
"Almost everyone now has access to the kind of expertise that was in very short supply until maybe even a few months ago," he says. More team members can investigate issues themselves rather than queuing up requests for a small group of experts.
Stewart wants to push this further into code creation itself, so developers can address security issues as they build rather than discovering them later. The vision is "expert level cyber capability to everyone in the development team."
This feels like the natural endpoint of tooling that compresses expertise. If you can give every developer on your team reverse-engineering capabilities that previously took years to develop, you shift the economics of when and how security work gets done.
What OpenAI Daybreak actually is
The source material is light on technical details about Daybreak itself—it's described as giving "defenders access to OpenAI models for cybersecurity work," which suggests purpose-built tooling or access rather than just GPT-4 with a system prompt.
Given the timeline (Stewart references capabilities available "maybe even a few months ago"), this seems tied to OpenAI's broader push into domain-specific applications. The model likely has training or fine-tuning optimized for reverse engineering workflows, code analysis, and vulnerability research.
What we don't know: whether Daybreak is a distinct model, a service layer on top of existing models, or something in between. OpenAI has been increasingly opaque about model architectures and training approaches, so we're probably not getting those details anytime soon.
The defender's advantage question
Stewart frames this as "shifting the advantage to defenders," which is the optimistic read. Defenders have always been resource-constrained relative to the threat landscape. Tools that let them do more with the same headcount are unambiguously good.
But let's acknowledge the obvious tension: if OpenAI's models can reverse-engineer malware at human-level quality, that capability isn't magically unavailable to attackers. The same models that help defenders understand threats faster can help attackers develop more sophisticated malware or identify vulnerabilities more quickly.
The hope—and it's only a hope—is that defensive use cases benefit more from the democratization of expertise than offensive ones do. Defenders are trying to understand and classify threats at scale. Attackers are trying to craft novel exploits that evade existing defenses.
If model-assisted reverse engineering helps defenders build better detection and response at scale, that might genuinely shift the balance. But that assumes attackers don't find equally valuable applications for the same capabilities.
What this means for the security researcher role
If you're a security researcher today, the shift from "six weeks per sample" to "eight hours per sample" probably feels somewhere between exciting and threatening.
The optimistic case: your job becomes more interesting. You spend less time on mechanical reverse engineering and more time on synthesis, pattern recognition, and strategic decisions. You investigate 40x more malware and develop much richer mental models of the threat landscape.
The pessimistic case: the market value of deep reverse-engineering expertise takes a hit. If tooling can compress that skill, organizations need fewer people who have spent years developing it.
My guess is we end up somewhere in between, with an awkward transition period. The best researchers will find ways to leverage these tools to do work that wasn't previously possible. Demand for "pure" reverse engineering skill will decline, but demand for researchers who can orchestrate model-assisted analysis at scale and connect findings across large sample sets will grow.
That's a different skill set, though, and not everyone will make the transition cleanly.
The broader AI-for-security trajectory
SentinelOne's use of Daybreak fits into a broader pattern: AI capabilities reaching the point where they can meaningfully accelerate expert work in security domains.
We've seen similar dynamics in vulnerability research, incident response, and threat hunting. Models are getting good enough to handle substantial chunks of workflows that previously required deep expertise and manual effort.
The next few years will reveal whether this genuinely shifts the defender's advantage or just raises the baseline sophistication required on both sides. My suspicion is that it compresses timelines across the board—defenders respond faster, but attackers also iterate faster—and the real winners are organizations that figure out how to operationalize these capabilities at scale.
For now, though, collapsing six weeks to eight hours is a hell of a demo. And if it lets security teams afford "a kind of intelligence that we were never able to afford before," that's worth paying attention to.