Best practices for open-source investigations: a conversation with Matt Ashburn

In NeedleStack’s 100th episode, Matt Ashburn shares OSINT best practices to avoid attribution mistakes, protect investigators, and validate sources with confidence.

Open-source investigations move fast, and that speed can quietly work against you. In OSINT, the mistakes that compromise an investigation are often small, routine, and easy to miss in the moment.

In a recent episode of NeedleStack, hosts AJ Nash and Robert Vamosi sat down with Matt Ashburn, Chief Customer Officer at Authentic8 and former NeedleStack host, to mark the show’s 100th episode and unpack the habits that most often expose investigators during open-source research. They explore why investigators get “burned,” how behavioral patterns can reveal more than technical indicators, and why primary-source validation is becoming harder — and more important — in an AI-accelerated environment.

Below are some key takeaways from the episode. And if you’re working investigations or OSINT collection at any scale, it’s worth listening to the full conversation below.

OSINT rarely fails because of one big mistake

Ashburn’s core premise is simple: Most investigations don’t collapse because of a dramatic failure. They erode because of small habits that become normalized: quick lookups, rushed clicks, and moments where you “just check something” outside a controlled workflow.

That’s why he emphasizes building repeatable processes that reduce the chance of accidental attribution. It’s not enough to know the right practices in theory. Under pressure, investigators default to convenience.

A common example: opening an untrusted link in a native browser on your everyday machine. Nothing visibly breaks. No alert fires. But the investigation’s defensibility starts to degrade — and you may not recognize the damage until much later.

The takeaway: Treat OSINT safety as a discipline, not a tool choice. Your environment and your habits both matter.

Separate your investigation environment from your real life

One of the clearest themes in this episode is compartmentalization: keeping investigative activity isolated from personal and corporate identifiers. Nash draws a contrast between government environments, where segmented systems made it harder to blur lines, and today’s private-sector reality: a single machine, constant multitasking, and “normal life” bleeding into investigative work.

Even when investigators avoid obvious mistakes like personal email, there are subtler breadcrumbs:

• Checking local weather
• Looking up local sports scores
• Browsing patterns that reveal time zone or geography
• Returning repeatedly to familiar sites and workflows
   

These small actions can create an attribution trail that links back to the investigator or their organization. The point isn’t paranoia; it’s recognizing that everyday behavior can carry identifying signals.

Behavioral attribution is often the overlooked risk

When people think about getting “burned” in OSINT, they often focus on technical exposure: IP address, browser fingerprinting, device characteristics, and network indicators.

Ashburn argues there’s a second bucket that’s just as important: behavioral attributes.

Your behavior can be a signature. Investigative workflows often look nothing like normal browsing. For example, landing on a site and immediately jumping to “About,” “Contact,” “Policy,” linked social accounts, and then pivoting into lookups is efficient, but it can also be a tell. Platforms don’t need advanced detection to notice intent when the browsing pattern is consistently investigative.

OSINT tradecraft has to account for that reality. Obfuscation isn’t only about where you appear to be. It’s also about how you act once you’re there.

CAI datasets can accelerate work, but they can’t replace corroboration

The conversation also touches on the growing use of commercially available information (CAI) datasets and threat intelligence databases. These sources can be powerful accelerators, especially for triage and lead generation.

But Ashburn raises a concern: When teams rely on CAI datasets without verification, investigations become fragile. Data can be incomplete, outdated, misattributed, or stripped of context. The danger isn’t that CAI is useless — it’s that it becomes a single point of failure when it’s treated as definitive.

The recommended approach is closer to classic tradecraft:

• Use datasets as a starting point
• Treat hits as leads, not conclusions
• Follow the thread back to the original source wherever possible
• Corroborate across independent sources
   

That’s increasingly difficult in high-volume environments like KYC and compliance workflows — but those are also the workflows where mistakes can create real harm, including reputational risk for the organization and downstream consequences for individuals impacted by incorrect decisions.

AI makes speed easier (and tradecraft harder)

When the discussion turns to AI, the tension becomes clearer: Organizations are under pressure to move faster, and AI can absolutely improve efficiency. But efficiency without integrity is a trap.

Ashburn’s view is that many organizations still haven’t figured out where the balance should sit — and that the answer won’t come from a tool alone. It has to be operationalized through process:

• When is AI appropriate for summarization or triage?
• What steps are required to validate outputs?
• How do teams preserve source provenance and context?
• What gets checked manually, and why?

Nash and Vamosi also point out a generational dynamic: As more analysts grow up “inside” technology, the instinct to trust outputs can become stronger, especially if they haven’t been trained to value primary sources the way traditional investigative disciplines demand.

The practical takeaway is that AI doesn’t eliminate tradecraft. It increases the importance of defining it.

Accountability improves accuracy — but it comes with tradeoffs

A particularly interesting thread is the role of accountability. Vamosi describes how journalism enforces rigor through reputation: Your byline is attached to your work, and getting it wrong carries consequences. In many OSINT contexts, outputs are packaged without individual attribution — which can weaken incentives to slow down and verify.

Ashburn argues that attaching names to analysis can increase accountability and drive better rigor, especially around confidence levels and analytic judgment.

At the same time, Vamosi notes a real constraint: Analyst attribution can create personal risk depending on the work and the adversary. That means organizations may need creative options (such as internal attribution systems) that build credibility and accountability without unnecessarily exposing individuals.

Tools can get you “to the doorstep,” but operators still need cultural fluency

The episode returns several times to a key idea: Technology can help you access the right places safely, but it can’t fully mask who you are if you behave like an outsider.

Ashburn uses a simple analogy: You can walk into a biker bar wearing the right jacket — but your questions, your cadence, and your lack of cultural fluency will still stand out.

Online investigations work the same way. The right platform can reduce technical exposure and help establish appropriate attribution, but investigators still need to understand:

• How communities communicate
• What “normal” behavior looks like in that environment
• Which actions raise suspicion
• When to slow down and avoid urgency-driven mistakes

The goal is risk mitigation, not perfection. You can’t eliminate every tell. But you can improve your odds by aligning tools, workflow, and operator behavior.

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