Latest / Cybersecurity Business with Fexingo: Security Companies, Breaches, and Enterprise Defense / How Security Teams Use Deception Technology to Trap Attackers
Transcript
- Lucas: If you think about how most security teams detect an attacker, it's usually after something bad has already happened — someone exfiltrated data, encrypted a server, or triggered an alert on a real system. But there's a growing playbook where you flip that dynamic entirely: you let the attacker think they've found something valuable, but it's actually a trap. Luna: You're talking about deception technology — honeypots, lures, breadcrumbs planted in real environments to mislead and detect attackers early. I feel like this used to be seen as a niche, almost spycraft approach, but it's getting more mainstream. Lucas: Exactly. And I want to anchor this in a specific case from early this year. A mid-sized financial services firm — about 1,200 employees, manages around $8 billion in assets — they deployed a deception layer across their environment. They planted fake database credentials in a shared network drive, set up a decoy Active Directory server with what looked like sensitive HR records, and seeded breadcrumb tokens that would beacon back if accessed. Luna: And did it work? Lucas: Within three weeks, they detected a ransomware group that had already gained initial access through a phishing email. The attackers spent about four hours inside the decoy AD server, pulling what they thought were admin credentials. Meanwhile, the security team had full visibility of their every move, cut off their command and control channel, and isolated the real systems before any encryption happened. The breach cost was essentially zero — just the time of the response team. Luna: That's a huge contrast to the average ransomware incident. IBM's cost of a data breach report puts the average at $4.5 million. So even if the deception platform costs $50,000 to $100,000 a year — which is typical for a cloud-based solution for a company that size — the ROI is almost immediate if it catches one real attack. Lucas: Right. And that's the economic argument that a lot of CISOs are making to their boards now. But the technology itself has evolved a lot. It's not just a honeypot sitting in a dark corner of the network anymore. Modern deception has three layers that work together. Lucas: Layer one is low-interaction honeypots — these are decoy servers, databases, or applications that mimic real services but don't actually process real traffic. They're easy to deploy and maintain. Layer two is high-interaction lures — these are more convincing, often with fake data that looks like actual customer records or financial reports. And the third layer is breadcrumbs: little pieces of fabricated data planted inside real systems, like a fake VPN configuration file or a spreadsheet with fake passwords, that when accessed, trigger an alert. Luna: I've heard some security architects argue that breadcrumbs are the most effective because they're planted where an attacker would naturally look once they've gained a foothold — on shared drives, in local credential stores, in browser caches. It's a low-cost way to get high-fidelity alerts. Lucas: And the key metric is mean time to detect. Most organizations still measure detection in days or weeks. With a well-designed deception layer, that can drop to minutes. Because the moment an attacker touches a decoy asset, you know exactly where they are, what tools they're using, and what they're after. Luna: But I want to push back a little. Skeptics say deception only works against unsophisticated attackers — script kiddies or low-level ransomware groups. A state-sponsored actor would see through a honeypot immediately, right? Lucas: That's a fair point, but evidence from the last couple of years suggests otherwise. In 2025, there was a well-documented incident where a state-sponsored group — attributed to a nation-state with advanced capabilities — spent 48 hours inside a decoy Active Directory server belonging to a defense contractor. They enumerated users, tried Kerberos attacks, even attempted to dump what they thought was the NTDS database. It was only after they tried to use a planted golden ticket that they realized it was fake. Luna: So even sophisticated attackers can be fooled if the deception is realistic enough and matches their expectations of the environment. Lucas: Exactly. The rules of engagement change when you're the one setting the stage. But it's not a silver bullet — deception works best when it's part of a layered defense alongside EDR, network segmentation, and strong identity controls. Lucas: And I think that's actually a really good lens for thinking about the value of this show. If these kinds of tactical breakdowns are useful for what you're building or running, that's exactly why we do this — to give you concrete approaches you can actually use. Luna: Absolutely. And it's because we keep the show ad-free that we can go deep on stuff like this without any sponsor noise. If today was actually useful to you, the way these stay ad-free is listener support — buy me a coffee dot com slash fexingo. Lucas: Yeah. No pressure, but every coffee helps keep the lights on and the topics practical. So back to deception — let's talk about how you actually measure success beyond just catching an attacker. Lucas: One framework I've seen from a Gartner report is to track three things: first, the number of deception-driven alerts that lead to a confirmed incident — that's your true positive rate. Second, the reduction in mean time to detect compared to your traditional detection stack. And third, the number of attacker techniques you observe in the decoy environment that you wouldn't have seen otherwise. Luna: That third one is interesting because it feeds back into threat intelligence. If you see an attacker using a new PowerShell obfuscation technique inside a honeypot, you can write a detection rule for it on your real endpoints before they ever get there. Lucas: Exactly. And that's the flywheel effect. The more you deploy deception, the more you learn about the adversary's playbook, and the better your overall defenses become. Some security teams are even using AI to dynamically generate decoys that adapt to the attacker's behavior in real time — if they start probing for SQL databases, the AI spins up a fake SQL server with convincing data. Luna: That sounds like it could scale well, but also introduces complexity. What about operational overhead? If you have a thousand decoy assets, who manages them, and how do you avoid false positives where a legitimate user accidentally steps on a breadcrumb? Lucas: That's a real concern. Most commercial deception platforms handle the lifecycle automatically — they spin down decoys that aren't being touched, rotate credentials, and keep the environment fresh. And they integrate with your SIEM and SOAR so that alerts are triaged based on context. As for false positives, you can whitelist known administrative tools and user accounts. Modern platforms have a false positive rate under 1 percent if configured well. Luna: One percent is pretty good compared to some signature-based tools that drown you in alerts. So who is actually adopting this today? Is it still mostly financial services and defense, or is it spreading? Lucas: It's spreading fast. Healthcare is a big growth area because of the sensitivity of patient data and the rise of ransomware targeting hospitals. Retail and e-commerce are also adopting it to protect payment card data. Even some mid-market companies with 200 to 500 employees are starting to deploy cloud-based deception as a service because the cost has come down significantly. Luna: So if a CISO is listening and thinking about where to start, what's the first step? Buy a platform? Or can you do something lightweight with open source tools? Lucas: You can absolutely start small. There are open source honeypot projects like Cowrie or T-Pot that let you set up a few decoys on a low-cost server. But for enterprise-scale, a commercial platform gives you the automation, integration, and maintenance that makes it sustainable. My advice: start with a single use case — protecting a specific critical asset, like your domain controller or a database — and prove the concept before expanding. Luna: It's like setting up a tripwire in the dark. The attacker doesn't know it's there, but when they cross it, you see them clearly for the first time. Lucas: Well put. And that visibility is the whole point. Deception doesn't stop the initial breach — phishing will still happen, vulnerabilities will still be exploited — but it dramatically compresses the time window between when an attacker gets in and when you know they're there. And in security, time is the most valuable resource you have.