Latest / Cybersecurity Business with Fexingo: Security Companies, Breaches, and Enterprise Defense / How Security Teams Are Using Deception Technology to Trap Attackers
Transcript
- Lucas: So there's this scene in almost every heist movie where the crew breaks into a vault, and they think they've got the goods — but it turns out the real diamonds were hidden behind a false wall, and what they stole was a dummy bag full of lead weights. Luna: Classic misdirection. And in cybersecurity, that's essentially what deception technology does — except the attackers don't get to walk away with lead weights; they get caught. Lucas: Exactly. Deception technology is one of the more interesting shifts I've seen in enterprise defense over the past few years. Instead of just building higher walls, you're planting decoys — fake servers, fake credentials, fake data — that look real enough to lure an attacker out of hiding. Luna: And you're not waiting for a signature or anomaly score to trigger. The moment someone touches that decoy, you know you have an active intruder. Lucas: Right. And that's the key difference from traditional detection. Most tools rely on pattern matching — 'this behavior looks suspicious.' Deception flips it: 'only an attacker would try to log into this account, because no legitimate user knows it exists.' Lucas: I want to ground this in a real example. I spoke with the CISO of a mid-sized healthcare company — about 4,000 employees, a few regional hospitals — and they deployed deception after a near-miss ransomware attack last year. Their setup included a decoy Active Directory domain controller and about 200 fake user accounts seeded with convincing data. Luna: And how long until it paid off? Lucas: Eight days. An attacker — likely initial access broker — used a compromised VPN credential, moved laterally, and tried to query what looked like a domain admin account. That account was a decoy. The deception platform alerted the SOC within 30 seconds, and they isolated the workstation before any ransomware could deploy. Luna: That's the dream scenario for a CISO. But I wonder — if the decoys are too obvious, won't experienced attackers spot them? Lucas: That was the knock against early honeypots for years. They were static, or they had telltale signatures — unrealistic network latency, stale data. Modern deception platforms use machine learning to generate dynamic lures that mirror the actual environment. If your real servers run Windows Server 2022 with specific patch levels and service accounts, the decoys will match those characteristics. Lucas: And it's not just servers. You can plant fake database records — say, patient records with plausible names and dates of birth — so if an attacker starts exfiltrating data, you get alerted the moment they touch a decoy record. Luna: So it's active, not passive. You're inviting the attacker to self-report. Lucas: Exactly. And that's the kind of proactive thinking that makes these conversations worthwhile. If these insights are useful for what you're building or defending, we'd love your support. It keeps this show ad-free and independent. You can help at buy me a coffee dot com slash fexingo. Luna: Yeah, every bit goes straight to keeping the episodes coming. Now, back to deception — what kind of budget are we talking about for a deployment like that healthcare company's? Lucas: For a 10,000-employee enterprise, a full deception deployment — covering endpoints, network, Active Directory, and cloud — runs roughly $150,000 annually. That's licensing, infrastructure, and managed service support. Compare that to the average cost of a breach, which IBM's 2025 report pegged at $4.5 million for healthcare specifically. Luna: So the ROI equation is pretty stark. Even if deception only prevents one serious breach every few years, it pays for itself many times over. Lucas: Absolutely. And we're seeing CISOs allocate 5 to 10 percent of their security budgets to deception now — up from basically zero five years ago. I talked to the deputy CISO at a major European bank, and he said their deception layer caught two separate ransomware attempts in 2025 that their EDR had missed. In both cases, the attacker used living-off-the-land techniques that didn't trigger behavioral alerts. Luna: Living off the land — that's when attackers use built-in system tools, right? So they don't drop malware. Lucas: Right. PowerShell, WMI, scheduled tasks — all legitimate tools. EDR tools have gotten better at detecting malicious use of those, but it's still a cat and mouse game. Deception doesn't care what tool you're using; it cares that you're touching something only an attacker would touch. Luna: So what's the catch? There's always a catch. Lucas: The biggest challenge is operational overhead. You have to maintain the decoys so they stay realistic. If your real environment gets patched but your decoys don't, an attacker can spot the inconsistency. Some platforms automate that, but it's not fully hands-off yet. Luna: And I imagine there's also a risk of false positives — what if a legitimate admin accidentally stumbles into a decoy? Lucas: It happens. But most platforms allow you to exclude certain IP ranges or user accounts. And in practice, if a legitimate user triggers a decoy, it's usually because of a misconfigured script — which is itself a useful signal. You get a free audit of your own processes. Lucas: There's also the question of scale. Deploying decoys across a hybrid cloud environment — AWS, Azure, on-prem — is more complex than a flat on-prem network. But the major vendors like Fidelis, Attivo, and Illusive have cloud-native offerings now. Luna: And where do you see this going next? We've talked about AI for threat detection, but deception feels like a different paradigm. Lucas: I think we'll see deception integrated more tightly with SOAR platforms. So when a decoy fires, the response can be automated — isolate the endpoint, revoke the session, even feed fake data to the attacker to buy time. Some organizations are already doing that. Luna: Feeding fake data — that's almost counter-espionage. You're not just defending; you're actively misleading the attacker. Lucas: Exactly. And it raises interesting ethical and legal questions — if you feed an attacker fake patient records, is that entrapment? But from a pure defense standpoint, it's incredibly effective. Lucas: The broader point is that deception forces attackers to work harder. They can no longer assume that every resource they see is real. That uncertainty alone raises their cost of attack. Luna: And for enterprise defenders, that's a huge win. Especially when budgets are tight and you need maximum leverage per dollar. Lucas: Right. Deception isn't a silver bullet — no single tool is. But as part of a layered defense, it's proving its value in real incidents. The healthcare CISO I mentioned told me they're now expanding their deception coverage to include cloud workloads and third-party integrations. Luna: So the trend is clear: deception is moving from niche to mainstream. I'm curious to see how the attacker community adapts. Lucas: They will. They always do. But for now, the advantage is with the defenders — as long as they keep their decoys fresh and their playbooks sharp.