[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fg6r9S2nxA_44vgzpnHR7ALaa1NnpALnFtr14fvVq_OQ":3},{"lesson":4},{"id":5,"slug":6,"article_id":7,"title":8,"body":9,"prevention":10,"framework_refs":11,"framework_notes":20,"status":21,"created_at":22,"published_at":23,"article":24,"tags":28,"podcasts":47,"controls":48},"4d36f279-75cb-4967-8a65-9dd465d6045f","ai-honeypots-turn-the-tables-on-scammers-by-wasting-their-time-and-harvesting-intel","ffb775ff-1f8f-4e29-9bfa-a7eb9abe4530","AI Honeypots Turn the Tables on Scammers by Wasting Their Time and Harvesting Intel","Traditional defenses focus on blocking threats after they reach potential victims, but AI-driven counter-scam bots like those developed by Apate take a proactive stance by engaging scammers directly and disrupting their operations. By mimicking real victims, these bots consume scammers' limited time and resources while simultaneously collecting actionable intelligence such as fraudulent URLs and financial account details. This matters because scam operations are volume-driven businesses — reducing their efficiency and exposing their infrastructure can meaningfully degrade their ability to harm real people. The intelligence gathered also feeds broader cybercrime investigations, helping law enforcement and organizations stay ahead of evolving tactics. This emerging approach highlights how offensive-minded, automated tools can complement traditional defensive security measures.","**Immediate actions:**\n- Educate employees and customers about common scam tactics (phone, email, and SMS) so they recognize and report suspicious contact attempts early.\n- Deploy call and message filtering tools that use AI or known-pattern databases to flag and block suspected scam communications before they reach end users.\n\n**Long-term improvements:**\n- Integrate threat intelligence feeds from counter-scam operations (like those run by Apate) into your SIEM or threat intelligence platform to proactively block known scam infrastructure.\n- Establish a formal fraud reporting and tracking program that aggregates scam intelligence internally and shares indicators of compromise with industry partners and law enforcement.\n- Develop a social engineering incident response playbook that defines escalation paths, victim support procedures, and evidence-collection steps.\n\n**Detection & monitoring measures:**\n- Monitor for brand impersonation and lookalike domains using automated discovery tools, and set up alerts for unauthorized use of your organization's name in scam campaigns.\n- Log and analyze inbound communication patterns (calls, emails, chats) to identify coordinated scam campaigns targeting your organization or customers at scale.",[12,13,14,15,16,17,18,19],"CIS Control 14 – Security Awareness and Skills Training","CIS Control 17 – Incident Response Management","NIST SP 800-61 Rev. 2 – Computer Security Incident Handling Guide","NIST SP 800-50 – Building an Information Technology Security Awareness and Training Program","NIST DE.CM-1 – Network Communications Monitored","ITIL – Problem Management (proactive identification of recurring threat patterns)","GDPR Article 32 – Security of Processing (protecting individuals from fraud-related data exposure)","FTC Scam Reporting Framework – Consumer fraud intelligence sharing guidelines",{},"published","2026-10-10T14:20:34.116406+00:00","2026-10-10T14:20:33.99+00:00",{"id":7,"url":25,"slug":26,"title":27},"https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fai-is-getting-really-good-at-messing-with-cybercriminals\u002F","ai-is-getting-really-good-at-messing-with-cybercriminals-e83db6","AI Is Getting Really Good at Messing With Cybercriminals",[29,35,41],{"id":30,"name":31,"slug":32,"description":33,"color":34},"1732a005-556e-411c-a9db-5edec3058571","Logging & Monitoring","logging-monitoring","Missing logs, no alerting, blind spots","#a855f7",{"id":36,"name":37,"slug":38,"description":39,"color":40},"182e11d5-57c4-444e-8ec8-4682ad60261b","Incident Response","incident-response","Slow detection, poor containment, missing playbooks","#14b8a6",{"id":42,"name":43,"slug":44,"description":45,"color":46},"7261eb8f-acd4-4d93-a489-7fdd652ec0ea","Security Awareness","security-awareness","Phishing, social engineering, human error","#22c55e",[],[49,53],{"slug":50,"name":51,"problem":52},"deception","Deception and honeypots","An attacker who gets in quietly can explore the network for weeks before any alarm goes off.",{"slug":54,"name":55,"problem":56},"threat-intelligence","Threat intelligence","Attack campaigns reach your sector, suppliers or software before your team hears about them."]