In light of the recent news about Claude Mythos, customers have started to reach out with both general and board-level questions about Mythos and what AI-augmented attacks mean for Dropzone's current product and roadmap. The good news is that Dropzone's architecture is already built for exactly this scenario.
Why Claude Mythos matters for the AI SOC
A quick summary of Claude Mythos: Anthropic is releasing a model it claims is so good at finding exploits that it had to give it away to top cybersecurity companies (CrowdStrike, Palo Alto Networks, etc) before releasing it to the public so they could test their own products against it to find novel security vulnerabilities and patch them before Mythos was released to the public.
While Anthropic has chosen to gate Mythos behind Project Glasswing and restrict public access, that restraint won't last industry-wide. Other frontier labs are closing the gap fast, and open-weight models, which can be downloaded, run privately, and stripped of any safety guardrails, are already proliferating.
Within days of Google releasing Gemma 4 in early April, uncensored variants were appearing on public repositories. The question isn't whether less restricted models with similar capabilities will exist. It's when.
While the top cybersecurity and infrastructure companies will have patched their most recent releases by the time Mythos is released to the public, there are still major gaps:
- If Mythos is as powerful as Anthropic claims then companies like Palo Alto Networks, CrowdStrike, or Microsoft may still miss something that attackers will be able to exploit
- There are tons of other companies who will not have had the opportunity to test their bespoke software against Mythos
- These fixes still require patching by end users, which as we know can take days, weeks, or even months
- Models with similar capabilities but no safety restrictions or coordinated disclosure commitments are already emerging, and adversaries using them won't give defenders any head start at all
This is where the investigation layer becomes critical.
How an AI SOC exposes the undetectable
In my last Dropzone blog post, I talked about how Dropzone was able to escalate a suspicious defender alert to the top of the investigation queue, ultimately alerting the customer to the Axios supply chain attack. Dropzone was able to do this because it looks at every alert like a skilled human analyst and is not reliant on detections or behavior analysis to come to its verdict. The same will be true in the future with zero days exploited by Mythos: the traditional tooling will miss the severity of the initial action but there will still be breadcrumbs to follow.
Mythos will unleash a flood of zero days across companies not using the products of the few who got early access to Mythos and anything that slips through the cracks of their head start on testing. This is inevitable. Timelines for exploitation of newly disclosed vulnerabilities will compress dramatically.
However once attackers are in they'll still have to do something. Running a complete investigation on every alert will become even more critical as connecting the dots at scale becomes the name of the game. A seemingly low priority alert may be hinting at something far more nefarious.
Dropzone runs full end-to-end investigations on every alert from any system, asks questions like an elite analyst, and can query and ask questions of both included threat intelligence and any system you give Dropzone access to about the alert. So let's walk through what this looks like in practice.
What an AI-augmented attack looks like in practice
Our attacker isn't a nation-state or elite red team. They're a moderately skilled actor who, until recently, wouldn't have had the capability to pull off what comes next.
Our attacker uses an advanced LLM to gain initial access. The LLM exposes a unique zero day via vulnerability chaining to get access to a vulnerable system.
Because the entry was achieved through a novel exploit with no known signature, traditional detection methods have nothing to report on: no matching rule, no anomalous behavior to be picked up. The attacker is already inside before any tool has registered that something happened.
However, once in, the attacker needs to pull down their tooling and abuse a native OS binary to cover their footprints. The EDR pops an alert on suspicious process execution on a legitimate system binary, certutil.exe.
It's the kind of alert that gets deprioritized constantly. Certutil and similar binaries generate false positives all the time in enterprise environments, and admins use them legitimately.
This is the kind of alert that will sit at the bottom of a queue for hours while an attacker is doing legitimate harm.
However Dropzone investigates every alert as they come in, treating them as true positives until proven otherwise.
What questions does an AI SOC ask during an alert investigation?
Dropzone asks an array of questions over the course of the investigation. In the case of our suspicious process it might ask:
- What's the full command line and process tree?
- Has this host shown any other low-or-medium severity alerts in the last 48 hours that were individually dismissed?
- Is the destination IP/domain in any threat intel feeds, and if not, when was it registered and does it have any other characteristics of attacker infrastructure?
To answer these questions it will rely on its ability to collect evidence from different sources. For any system involved in this alert or data lakes containing valuable information Dropzone can create a query on the fly to answer these questions.
These queries aren't hard coded by the humans behind the curtain. We teach Dropzone to be an expert query writer and give it tips but the queries are flexible and dynamic.
The answers come back and they aren't looking good:
- Process tree looks automated. The certutil call wasn't launched by a user clicking something. It was spawned by cmd.exe, which was spawned by wmiprvse.exe (WMI provider), which means something executed it remotely.
- Other dismissed alerts on the same host. There was a failed login alert 6 hours ago that was closed as routine, and a new scheduled task creation 2 hours ago that was classified low severity.
- Destination is suspicious infrastructure. The domain was registered 9 days ago, is hosted on a VPS provider popular with threat actors, has no legitimate web content, and doesn't appear in any threat intel feeds yet.
With this evidence (and much more) Dropzone is able to confirm that this alert is a true positive and needs urgent attention. Dropzone alerts the relevant humans with a fully investigated alert.
Analysts can skip the hour of manual triage and go straight to response:
- Isolate the host
- Block the command and control infrastructure
- Start scoping the blast radius across the environment
These responses can be done directly from the Dropzone console or fed into a SOAR tool.
How do you find compromises when there's no visible source?
But there's still an open question in this scenario. The analyst has a confirmed compromise and a contained host but they don't know how the attacker got in.
The initial entry point is invisible. There's no alert that explains it. That's what a zero-day looks like from the defender's side: post-compromise activity with no visible source.
This surfaces a harder problem: how do you proactively find whether you've already been compromised, and where your weak points are? As AI-generated exploits compress attack timelines, proactive hunting becomes table stakes.
When novel zero-days are the entry vector, you can't rely on detection rules to trace the origin. You have to go looking. Historically, that meant a dedicated threat hunting team or an MDR contract, and most organizations don't have those resources.
Why proactive AI threat hunting is now table stakes
Dropzone's AI Threat Hunter, entering beta soon, is built to close this gap. Threat Hunter runs proactive, AI-driven hunts across your environment around the clock, building novel hunts as new threat intelligence emerges, whether the attack vector is AI-generated or not.
It does two things well:
- Finds attack vectors and delivers actionable recommendations to close them
- Surfaces issues like visibility gaps, misconfigurations, vulnerabilities, and policy violations
As the bar for launching novel attacks gets lower, Dropzone AI Threat Hunter brings the bar for threat hunting down to match.
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