{"id":15995,"date":"2026-03-24T07:07:40","date_gmt":"2026-03-24T07:07:40","guid":{"rendered":"https:\/\/newestek.com\/?p=15995"},"modified":"2026-03-24T07:07:40","modified_gmt":"2026-03-24T07:07:40","slug":"why-cisos-should-embrace-ai-honeypots","status":"publish","type":"post","link":"https:\/\/newestek.com\/?p=15995","title":{"rendered":"Why CISOs should embrace AI honeypots"},"content":{"rendered":"
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The nightmare begins with our protagonist trying to find a way inside to get to the firm\u2019s files, but every door is bolted shut. Then they spot a back entrance and they\u2019re in, first walking, then running down one corridor, then another, and another, feeling that they\u2019re getting ever closer to that file and a payday they\u2019ve dreamt about for years. But something doesn\u2019t feel right. The corridors, it seems, just lead to more corridors. For the first time, our protagonist feels like they\u2019re being watched. And so, they flee.<\/p>\n

It\u2019s a nightmare that plays out every couple of minutes in companies around the world: a cybercriminal pings what they think is a company\u2019s exposed server, only to discover that the seemingly sensitive information it\u2019s sending back is anything but. That\u2019s because what they\u2019ve actually encountered is a honeypot server, a digital cage used by organizations to lure threat actors and capture their movements as they try to break into the company.<\/p>\n

In theory, this is much safer than letting them break in while learning something from the damage they leave behind. In practice, however, the effectiveness of honeypots has historically depended on how much effort its programmers put into making the environment seem realistic to the attacker \u2013 which, considering such servers can cost tens of thousands of dollars per month to maintain, isn\u2019t usually much. But the recent pairing of large language models (LLMs) with honeypots allows these servers to generate convincing environments at a fraction of the cost, supercharging the acquisition of threat intelligence for both individual organizations and the cybersecurity community at large.<\/p>\n

How honeypots have been used<\/h2>\n

Honeypots themselves have been around since 1986, when the astronomer-turned-computer systems manager Dr Cliff Stoll ensnared<\/a> a KGB spy attempting to steal US military secrets via an ARPANET connection. Stoll\u2019s innovation would eventually inspire classic honeypot servers.<\/p>\n

\u201cResearchers love them [as] they\u2019re one of the best ways to collect real-world attacker TTPs and discover new malware campaigns,\u201d says cybersecurity researcher and founder of Beelzebub Mario Candela. SOC teams, meanwhile, tended to see them as \u201cnice to have,\u201d given how difficult and expensive honeypots were to deploy and maintain \u2013 premium versions absorbed thousands of dollars and engineering hours per month \u2013 and how quickly the more sophisticated, dangerous threat would identify them.<\/p>\n

The emergence of LLMs in the late 2010s, however, would lead to the first experiments by academic researchers in combining AI in honeypots. Dr M. Abdullah Canbaz remembers this period well. An assistant professor in information sciences and technology at the University at Albany, SUNY, the idea of bolting an LLM onto a honeypot came from one of his students. The pair built their own LLM, training it to parse traffic data and handle a huge variety of Linux commands. This, explains Canbaz, allowed it to grapple with even the most sophisticated hacker. The resulting paper<\/a> was published in 2024, at the peak of an efflorescence of academic<\/a> interest<\/a> in AI-powered honeypots. \u201cI\u2019ve got so many calls since then,\u201d says Canbaz, often from people who \u201cwant to take our paper and\u2026 turn it into a startup business.\u201d<\/p>\n

Many have. Far from being an academic exercise, AI-powered honeypots are now being built by organizations large and small. On the smaller end is Beelzebub a low-code, open-source AI-powered honeypot that has acquired a reputation for devilish effectiveness. \u201cThe key architectural leap was integrating LLMs directly into the deception layer,\u201d says Candela. \u201cInstead of static, rules-based honeypots, we built high-interaction, LLM-driven deception environments that can dynamically respond to attackers, keeping them engaged for longer and capturing richer intelligence.\u201d<\/p>\n

Sophisticated attackers will probably cotton on eventually, but the benefit for cybersecurity teams make it worth trying. They \u201cmay eventually notice subtle inconsistencies: perhaps a response latency pattern that differs from a real system, or a file system that\u2019s too \u2018clean,\u2019 or a system that fails to exhibit certain expected side effects of a real compromise,\u201d says Candela. But \u201cby the time an attacker starts to suspect they\u2019re in a deception environment, we\u2019ve already captured their tooling, TTPs, and intent.\u201d<\/p>\n

Why CISOs should consider honeypots<\/h2>\n

Another player in the AI honeypot space is Deutsche Telekom (DT). The firm is both a user and purveyor of AI-powered honeypots through its free, open-source platform \u2018T-Pot<\/a>.\u2019 The most obvious advantage to their use, explains Marco Ochse, DT\u2019s lead for threat analytics and mitigation, lies in how little these traps cost to set up and run compared to their antecedents. \u201cIn practical terms, AI changes the economics of deception,\u201d says Ochse. \u201cIt allows [the organization] to scale believable interaction without [the usual] cost and complexity.\u201d<\/p>\n

That doesn\u2019t come at the expense of complexity, adds DT\u2019s chief security officer, Thomas Tschersich. As far as the engineer behind the honeypot is concerned, the difference between the classical and the AI-powered variety is similar to filming a movie scene using complex wooden sets constructed on a back lot or CGI: both are facades, but the latter is much less expensive while remaining nigh-on indistinguishable from a fake city street painstakingly constructed out of plywood. Even better, the AI-powered honeypot can adapt to the requests of the hacker in real time, making it more likely they\u2019ll stay in the trap for longer periods without realizing they\u2019re in one in the first place. In the end, says Tschersich, you can raise the authenticity of interactions with threat actors without this being associated with high investments.<\/p>\n

That\u2019s become more important amid a spike in attacks on organizations that begin with threat actors having already obtained valid credentials to access systems. In these scenarios, says Candela, defenders \u201care blind once an attacker is inside\u201d the network. By keeping threat actors occupied at traditional attack points for longer and deploying AI-powered honeypots in less traditional locations, such as APIs and within AI agents, says Candela, organizations can steal a march on their opponents.<\/p>\n

What, then, are we all learning from the deployment of this larger, AI-powered net? The big development, explains Candela, is the use of AI by the cybercriminals themselves. It is \u201cdemocratizing attacks\u201d with threat actors now using coding assistants to not only rapidly generate and deploy exploit code at scale but also use AI to probe vulnerabilities in target systems automatically. \u201cOpen-source AI red-team tools mean autonomous agents can now scan, exploit and adapt without human input,\u201d says Candela.<\/p>\n

There are risks to this paradigm. LLM outputs are, after all, essentially the product of very high-level pattern recognition. Cede cybersecurity to this kind of AI, says Canbaz, and you risk leaving the attack surface wide open to exploitation by cybercriminals mounting unorthodox and, therefore, unexpected campaigns. In this future, he continues, \u201cthere\u2019s no clear definition of an attacker.\u201d<\/p>\n

How attackers may counter the honeypot trap<\/h2>\n

Candela shares these concerns, envisioning the emergence of \u2018deception detection-as-a-service\u2019 providers meeting demand from cybercriminal organizations to root out AI-powered honeypots in companies ahead of breach attempts. Additionally, \u201csophisticated actors might try to poison honeypot data or manipulate the deception layer,\u201d says Candela, a key reason why Beelzebub\u2019s own deception environment is isolated.<\/p>\n

The speed of cyberattacks may also increase as hackers, unaware if they\u2019re interacting with a honeypot or not, aim to conduct their nefarious business as quickly and efficiently as possible just in case they\u2019re being watched. \u201cThis actually makes deception more valuable, not less,\u201d says Candela, \u201cbecause speed-focused attackers are more likely to interact with well-placed honeypots during rapid lateral movement.\u201d<\/p>\n

Time, then, to say goodbye to the classic honeypot? Not necessarily, argues Tschersich. \u201cStatic honeypot deployments such as low-, medium- or high-interaction sensors will not be replaced but complemented by AI-powered honeypots in response to a highly automated and AI-driven threat landscape,\u201d he says. Even so the cybersecurity landscape is changing rapidly, with responsibility for attack and defense increasingly shouldered by machines. The AI-powered honeypot, perhaps, is a bridge to that future \u2013 for good and ill.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The nightmare begins with our protagonist trying to find a way inside to get to the firm\u2019s files, but every door is bolted shut. Then they spot a back entrance and they\u2019re in, first walking, then running down one corridor, then another, and another, feeling that they\u2019re getting ever closer to that file and a payday they\u2019ve dreamt about for years. But something doesn\u2019t feel… <\/p>\n

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