Latest / Cybersecurity Business with Fexingo: Security Companies, Breaches, and Enterprise Defense / How Security Teams Use Deception Technology to Trap Attackers
Transcript
- Lucas: Luna, I want to talk about something that sounds almost too simple to be effective — but some of the smartest security teams I've talked to are betting big on it. It's called cyber deception, and the basic idea is this: you plant fake assets inside your network — fake servers, fake databases, fake credentials — and you wait for an attacker to touch one. Luna: Right, like digital tripwires. Honeypots have been around forever, but I get the sense this is different now. Lucas: Exactly. Traditional honeypots were usually isolated systems sitting outside the production network, and they were pretty easy for sophisticated attackers to spot. What's changed in the last few years is that deception technology is now woven directly into the real network. The decoys live alongside actual production assets, they're configured to look exactly like what an attacker would be hunting for, and they trigger alerts the moment anyone — or anything — interacts with them. Luna: So it's not about luring attackers into a sandbox anymore. It's about creating a layer of lies that sits on top of reality. Lucas: That's a great way to put it. And the reason it's gaining traction right now is that traditional detection tools — EDR, network monitoring, SIEM — they all rely on knowing what malicious behavior looks like. But attackers are getting better at flying under the radar. Deception flips the problem: instead of looking for bad behavior, you create a bunch of things that no legitimate user or system should ever touch. Any interaction with a decoy is by definition suspicious. Luna: I've heard the statistic that something like ninety-nine percent of breaches involve some form of lateral movement — attackers creeping through the network after initial access. Deception seems perfectly designed to catch that phase. Lucas: That's exactly the use case. I talked to a mid-sized financial services firm recently — about four thousand employees, typical hybrid environment with on-prem and cloud. They had deployed a standard EDR solution, but they were still seeing gaps. So they rolled out a deception platform from a company called Illusive Networks, and they planted about five hundred decoy endpoints across their environment. Within forty-eight hours, the system caught an advanced persistent threat that had been inside their network for weeks — the EDR had flagged nothing. Luna: That's a pretty dramatic example. What was the attacker doing? Lucas: The attacker had compromised a single user's workstation through a phishing email, then started scanning for file shares and database connections. One of the decoy shares looked like it contained financial reports — the attacker bit, and immediately the deception system alerted the security team. They isolated the affected machine, traced the initial entry point, and ejected the attacker within hours. The whole incident went from discovery to remediation in under four hours, compared to what could have been weeks of dwell time. Luna: So the decoys basically bought them time. And I imagine the economics work out pretty well — deploying software-based decoys is cheap compared to the cost of a breach. Lucas: That's the argument a lot of CISOs are making. The typical deception deployment costs somewhere between fifty thousand and two hundred thousand dollars a year, depending on scale. Compare that to the average cost of a data breach, which IBM's latest report pegs at over four and a half million dollars globally. Even if deception only catches one major incident every few years, it pays for itself many times over. Luna: And it's not just the financial return — it's the confidence it gives the security team. Knowing that any interaction with a decoy is a guaranteed detection event must be a huge morale boost. Lucas: Absolutely. One security operations manager I spoke with said that before deception, they felt like they were shouting into the dark. After they deployed it, they had a concrete signal — a 'red button' that they knew was real. It changed how they prioritized alerts. False positives dropped dramatically because the decoy alerts were inherently high confidence. Luna: Let's talk about the technology a bit. Are we just talking about fake servers, or has the category evolved? Lucas: It's evolved significantly. Modern deception platforms generate a whole ecosystem of fake assets: endpoints with realistic hostnames, operating systems, and running services; fake credentials scattered across memory and configuration files; even fake data — like document files with embedded tracking markers or database tables with realistic-looking customer records. Some platforms use generative AI now to create documents that look authentic enough to fool a human attacker who opens them. Luna: Generative AI for decoys — that's interesting. So instead of deploying a static set of fake files that an attacker could pattern-match, the system can dynamically generate new decoys based on what the attacker seems to be targeting. Lucas: Exactly. It's called 'adaptive deception.' The platform monitors network traffic, sees what kind of assets the attacker is probing for, and then generates decoys that match that profile. For example, if the attacker starts scanning for Windows file servers, the platform can spin up fake file servers with realistic share names and access patterns. The attacker thinks they're progressing deeper into the network, but they're actually being led into a dead end. Luna: That's almost like a psychological operation — you're manipulating the attacker's perception of the network. Lucas: Which is why some vendors now call it 'active defense' rather than just detection. You're not just waiting for an alert — you're shaping the attacker's behavior, buying time, and collecting intelligence about their tools and techniques. A company called TrapX has done some interesting work in this area, particularly around medical device environments where you can't patch or run traditional security software. They create decoy medical devices — fake MRI machines, infusion pumps — to lure attackers targeting healthcare networks. Luna: That's a fascinating vertical. And I imagine deception works especially well in operational technology and industrial control systems, where availability is critical and you can't just reboot or update every device. Lucas: Right. In OT environments, deception is one of the few detection strategies that doesn't interfere with operations. You can't install an agent on a programmable logic controller, but you can put a decoy PLC on the network that mimics the real one. The moment an attacker tries to send a command to what they think is a production controller, the alarm goes off. No disruption, no false positives from legitimate traffic. Luna: So the bar for false positives is essentially zero — because no legitimate operator should ever touch those decoys. Lucas: Exactly. And that's the core value proposition. In a typical SOC, analysts are drowning in alerts. Most of them are benign. Deception gives you a stream of alerts that are virtually guaranteed to be malicious. That changes the entire workflow. Instead of triaging thousands of events, analysts can focus on a handful of decoy hits and investigate them deeply. Luna: I can see why CISOs are starting to think of deception as a core detection layer rather than an add-on. It's like installing a tripwire in your house — it doesn't stop someone from breaking in, but it tells you immediately that they're there. Lucas: And that immediacy is critical. We talk a lot about dwell time — the window between initial compromise and detection. The average dwell time is still somewhere around two hundred days for many organizations. Deception can shrink that to minutes or hours. The firm I mentioned earlier went from weeks of undetected presence to a forty-eight-hour detection window. That's a dramatic shift. Luna: And if today's episode helped you think about your own security strategy, honestly, if it was worth a coffee to you, that's the link — buy me a coffee dot com slash fexingo. We keep the show ad-free, and listener support is what makes that possible. Lucas: Yeah, it's a small thing that goes a long way. And we genuinely appreciate it. Now, back to deception — I want to talk about some of the pitfalls, because it's not a silver bullet. Luna: Sure. What are the common mistakes? Lucas: The biggest one is poor decoy placement. If you put decoys on a network segment that no attacker ever reaches, you get zero detections and the project gets labeled a failure. You need to think about the attacker's path — where do they land after initial compromise? Typically that's user workstations. Then they move toward file servers, domain controllers, database servers. Your decoys need to be on those same segments, with believable names and configurations. Luna: So it's not just about number of decoys — it's about strategic placement based on threat modeling. Lucas: Exactly. Another mistake is not maintaining the decoys. If you deploy a set of decoys and then never update them, they become stale. Attackers share information — once a decoy is publicly identified, it becomes useless. You need to constantly rotate, update, and add new decoys to keep the environment fresh. Some platforms automate this, but it requires resources. Luna: And what about the risk of attackers using decoys to feed you false information? Could they trigger a decoy just to distract the SOC while they do something else? Lucas: That's a real concern. Sophisticated attackers might intentionally trigger decoys to cause a diversion. But that's actually a feature, not a bug — if an attacker is willing to burn their anonymity just to distract you, you've already forced them to change their plan. And a well-designed SOC should have playbooks for that scenario — investigate the decoy hit quickly, but also look for other concurrent activity. Luna: So it's about layering. Deception is powerful, but it needs to be part of a broader defense strategy. Lucas: Absolutely. No single tool solves everything. But I think deception addresses a fundamental weakness in most security programs: the inability to detect what you don't know to look for. And that's a pretty big gap to fill. Luna: Lucas, where do you see this heading in the next couple of years? Will deception become standard practice for most enterprises? Lucas: I think so. The technology is maturing, costs are coming down, and the threat landscape is getting harder to defend with signature-based tools alone. I also expect to see more integration between deception platforms and SOAR systems, so that decoy hits automatically trigger containment actions. And generative AI will make decoys more believable and adaptive. I'd be surprised if, by 2028, most mid-to-large enterprises don't have some form of deception in their stack. Luna: It's one of those rare ideas that sounds almost too simple — but the data and the stories from practitioners make a really compelling case. Thanks for digging into this one. Lucas: It's a fun topic. And I think it's a reminder that sometimes the best defense isn't just about building higher walls — it's about making the attacker not know which wall is real.