Latest / Cybersecurity Business with Fexingo: Security Companies, Breaches, and Enterprise Defense / How Enterprises Are Using Cyber Deception Grids
Transcript
- Luna: Lucas, I keep hearing about cyber deception grids. Are we talking honeypots from the 1990s, or something actually new? Lucas: It’s both older and newer than that. Honeypots have been around for decades, but the modern deception grid is a lot more systematic. Think of it as a network of fake assets — servers, databases, credentials — that look real enough to fool an attacker. The goal isn’t just to observe them. It’s to make them waste time, reveal their tools, and trigger an alert the moment they touch something fake. Luna: So you're baiting them into a trap. I imagine the tricky part is making the bait believable. Lucas: Exactly. If the decoy looks too perfect or too sloppy, the attacker smells something wrong. The best grids mirror the actual production environment. Same naming conventions, same OS versions, same patch levels. Some even populate fake data — for example, a fake customer database with thousands of realistic-looking records. The attacker lands on that and thinks they hit pay dirt. Luna: Let’s get concrete. Do you have a real deployment you can walk me through? Lucas: Yeah. I spoke with the CISO of a Fortune 500 retailer — I can’t name them, but the story is on the record at a conference. They deployed a deception grid after a near-miss ransomware attack in 2024. The attacker had been inside their network for six days before the security team caught them. That six-day dwell time was terrifying to the board. So they bought a grid from a vendor called Attivo Networks — now part of SentinelOne. Luna: Six days. That’s a long time for a ransomware crew to map out your environment. Lucas: Right. The deception grid was designed to cut that dwell time. They planted decoy Active Directory servers, fake file shares with names like ‘Q4_Budget.xlsx’, and a honeytoken — a fake admin credential that would trigger an alert if anyone ever tried to use it. The grid didn’t rely on signatures. It relied on the fact that an attacker can’t distinguish real from fake if the fake is good enough. Luna: Did it work? Lucas: About eight months later, a different group — a known ransomware affiliate — got in through a phishing email. The initial access was real, but within two hours, the attacker tried to use that fake admin credential against a decoy server. The grid generated an immediate high-fidelity alert. The response team contained the threat in under thirty minutes. Luna: Thirty minutes versus six days. That’s a massive improvement. But I imagine the grid also generates a lot of noise — false positives from legitimate users bumping into decoys? Lucas: Actually, that’s the beauty of it. If no legitimate process should ever touch those decoys, then any interaction is almost certainly malicious. The false positive rate is incredibly low compared to, say, an EDR rule that flags PowerShell usage. In this retailer’s case, they had exactly seven alerts from the grid in the first year. Two were red team exercises. Five were real attackers. Luna: So the signal to noise ratio is excellent. What about cost? Is this something a mid-size company can afford, or is it purely for the Fortune 500? Lucas: It’s come down a lot. Five years ago you were talking six figures for a proper grid. Now there are SaaS-based deception platforms like Illusive Networks and TrapX that start around thirty thousand dollars a year for a modest deployment. You don’t need a thousand decoys. Even ten well-placed decoys — a fake domain controller, a fake SQL server, a few honeytokens — can catch an attacker who’s doing manual reconnaissance. Luna: Interesting. So it’s becoming more accessible. I’ve also heard that some deception grids use machine learning to generate realistic decoys automatically. Is that a real trend? Lucas: It is. Vendors like Cymulate and Acalvio have started using ML to profile the real environment and generate decoys that match the exact software versions, hostnames, and network topology. That avoids the classic pitfall where a decoy runs Windows Server 2019 while everything else is 2022 — a dead giveaway to a skilled attacker. Luna: So the decoys become harder to spot. But what about the attacker who’s smart enough to avoid obvious traps? Do they check every machine before interacting? Lucas: Some do. They might ping a host, check its services, look for telltale signs like unusually open ports or default credentials. But modern grids counter that by making decoys respond exactly like real machines. They’ll respond to ICMP, show realistic banners, even simulate traffic with other decoys. The goal is to make the attacker commit time and resources. Every minute they spend probing a decoy is a minute they’re not exfiltrating data. Luna: Let’s talk about integration. How does a deception grid fit into a typical security stack? I imagine it needs to feed into a SIEM or SOAR to be useful. Lucas: Absolutely. The grid is only valuable if the alerts go somewhere and trigger action. Most modern platforms integrate via standard syslog or API to Splunk, Chronicle, or Sentinel. They also integrate with EDR — when a decoy triggers an alert, the EDR can automatically isolate the endpoint that touched it. That retailer I mentioned had the grid feed directly into their SOAR, which kicked off a playbook that blocked the source IP, notified the SOC, and started a forensic capture. Luna: That’s pretty slick. One concern I hear from CISOs is that deception grids could cause legal issues — if a decoy looks like real customer data and an attacker grabs it, could that be considered a data breach from a regulatory standpoint? Lucas: It’s a valid question. The general guidance is that the data inside decoys should be clearly fake — not resembling any real person’s PII. If you populate a decoy database with names like ‘Test User’ and fake Social Security numbers starting with 000, you’re fine. But if you accidentally include real data, yeah, you could have a problem. The vendors I’ve talked to emphasize that the burden is on the deployer to ensure all decoy data is synthetic. Luna: Makes sense. So it’s more about operational discipline than a flaw in the technology. Lucas: Exactly. And the upside is significant. Beyond detection, grids also provide threat intelligence. When an attacker interacts with a decoy, you can capture their IP, the tools they used, the commands they ran, even the malware they dropped. That intel can feed back into your other defenses or even be shared with an ISAC. Luna: I’ve seen that in practice. Last year, a healthcare system used its deception grid to capture a novel ransomware variant. They shared the hash with the health isac, and it helped other hospitals block it before it spread. Lucas: Right. So you’re not just protecting yourself — you’re contributing to collective defense. And that’s a shift from the older mindset of keeping everything secret. Luna: Let’s pivot slightly. What’s the biggest mistake companies make when they first deploy a deception grid? Lucas: They don’t test it. They buy a platform, sprinkle decoys around, and assume it’s working. But if the decoys don’t blend in — if they’re on a separate VLAN that no normal host would be on, or if they have no traffic to them — a good attacker will notice. You need to run red team exercises that specifically try to identify the decoys. That’s the only way to know if your camouflage is any good. Luna: Have you seen any specific red team exercises that exposed bad decoys? Lucas: Yeah. One I heard about: a company put decoy web servers on the same subnet as production, but they didn’t simulate background noise. So the decoys had zero traffic except from the red team. The red team literally just waited and watched which IPs never received any broadcast traffic. That told them exactly which hosts were fake. Luna: So you need to simulate normal background noise — ARP broadcasts, DNS queries, the whole thing. Lucas: Exactly. Good deception platforms include a ‘noise engine’ that generates realistic traffic. Without it, you’re basically hanging a sign on each decoy that says ‘I’m a honeypot.’ Luna: Let me ask you this — where do you see deception going in the next two or three years? Is it going to become standard in every SOC? Lucas: I think it will, especially as the cost drops and integration gets easier. The really interesting frontier is using deception inside cloud environments. Say you have an AWS account with hundreds of resources. You can plant decoy S3 buckets, decoy Lambda functions, decoy IAM roles. An attacker who tries to assume a decoy role triggers an instant alert. That’s powerful because cloud misconfigurations are so common. Luna: Cloud deception makes a lot of sense. The attack surface is huge, and traditional network-based detection doesn’t always translate. Lucas: And the monitoring gap is real. Most cloud security tools are good at detecting configuration drift, but not at catching an attacker who’s already inside using valid credentials. Deception fills that gap. Luna: I’d love to see a case study on that. Maybe we can find one for a future episode. Lucas: Let’s make a note. I know a few MSSPs that are starting to offer cloud deception as a managed service. That could be a whole episode on its own. Luna: Before we wrap, I want to be honest about something. We get to dig into these topics in depth because of listeners who support the show directly. Lucas: Yeah. We don’t run ads — no mid-roll for a VPN or a mattress company. That’s a choice. And the trade-off is that a small number of listeners who find these conversations useful chip in through buy me a coffee dot com slash fexingo. It’s not a big ask. Just if you’re getting value and want to keep it ad-free, that’s how it works. Luna: Right. And we mean it — no perks, no bonus episodes, no guilt. Just a way to support the work. Now back to deception. Lucas, you mentioned that some grids are starting to incorporate generative AI to create more convincing decoy content. Is that happening already? Lucas: It is, though it’s early. A few vendors use large language models to generate realistic-looking documents, spreadsheets, and even source code for decoys. The idea is that if an attacker opens a fake file named ‘employee_handbook_v5.pdf’, they should see something that looks authentic — not lorem ipsum. That obviously wasn’t possible a few years ago. Luna: So the decoys are getting smarter. But I wonder — could attackers use AI to better detect decoys? It feels like an arms race. Lucas: It is. There’s already research on using machine learning to fingerprint decoys by measuring response times or subtle differences in network behavior. But the defenders have an advantage: the attacker has to guess which of thousands of hosts are real, while the defender only needs a few convincing fakes. It’s asymmetrical. Luna: That’s a good note to end on. So deception grids aren’t a silver bullet, but they’re a powerful tool in the active defense toolkit. Lucas: Absolutely. If you’re a CISO dealing with long dwell times, it’s worth at least a pilot. The ROI can be measured in minutes saved during an active breach. Luna: Thanks, Lucas. Good conversation. Lucas: Thanks, Luna. Talk next time.