Future of Threat Intelligence

Unit 42's Andrew Rathbun on the Sysmon Configuration Mistake Enterprises Are Making

Andrew Rathbun, Senior Consultant at Palo Alto Networks Unit 42, has spent years tearing apart Windows endpoints across ransomware, APT, insider threat, and DPRK IT worker cases. His read on the state of enterprise Windows logging is blunt: most organizations have spent significant money on detection tooling while leaving the native forensic record so truncated that proving an intrusion timeline is nearly impossible. He introduces the "conveyor belt of volatility" as a forensic lens, every second, events fall off the back end of your log, and the default sizes Microsoft ships are a relic of 2002 disk economics. Accepting those defaults in a contemporary environment isn't a configuration oversight; it's a gift to the attacker.The conversation goes deep on the four artifacts Andrew calls his sysadmin Christmas list of Sysmon, the Security Event Log, Volume Shadow Copies, and the $J USN Journal, and why each is typically either absent, stale, or undersized when he arrives on a case. He also covers what DPRK IT worker cases look like from the endpoint, why EDR alert queues are generating true positives that go ignored for days, and how he actually uses AI on cases, including a specific example of generating a PowerShell script to convert Linux audit log epoch timestamps to human-readable time, a script he's been running in production for years.Topics discussed:The "conveyor belt of volatility" framework for understanding Windows event log retentionWhy accepting default log sizes actively shortens the forensic timeline available during incident responseWhy Sysmon's inclusion in Windows 11 is long overdue, how stale installations with outdated event IDs are a common unforced error in enterprise environmentsHow volume shadow copies can extend forensic visibility across months of attacker activityThe $J USN Journal as a file system ledger for every file creation, deletion, rename, and size change on a Windows partitionWhy EDR is a mandatory but insufficient control, including how alert fatigue causes true positives to be miscategorized as false positivesWhat DPRK fake IT worker cases look like from the endpoint, including the forensic value of USB artifact timestampsHow AI functions as a genuine force multiplier in DFIR while remaining unreliable as a source of authoritative forensic ground truthWhy GitHub fluency, not tool mastery, is the foundational skill for anyone entering digital forensicsKey Takeaways: Size the Windows Security event log to at least 1 GB. The default 32 MB cycles 4624/4625 events fast enough that authentication history from the week before your incident is already gone.Deploy Sysmon and keep it current. Treat version currency as a security control.Size the $J USN Journal appropriately on all Windows partitions. It's a file system ledger of every create, delete, rename, and resize.Enable volume shadow copies and treat retention depth as a forensic asset. Alert on event IDs 1102 and System 104. These signal security log and general event log clearing.Audit EDR queues for true positives closed as false positives. Baseline USB artifact timestamps and KVM device registry entries on remote worker endpoints. Use AI to parse unfamiliar log syntax and generate one-off scripts — not as forensic ground truth. Don't assume EDR coverage eliminates the need for native Windows logging. They capture different visibility layers.Build GitHub fluency as a foundational DFIR skill.

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Trend AI's Robert McArdle on Criminal Business Models Surviving Tech Revolutions

4w ago00:40:03Tap to summarize

After 18 years tracking cybercriminal operations at Trend AI, Robert McArdle, Director of Cybercrime Research, has developed a framework for predicting how threat actors adopt new technology: the answer consistently comes down to economics, not capability. He breaks down three rules of thumb his team uses: criminals want an easy life, any new technology must beat the ROI of their current model, and cybercrime is evolutionary rather than revolutionary. Those rules explain why ransomware has actually slowed the adoption of new attack methods and why the lowering technical barrier for attackers creates an asymmetric burden on defenders, who must demonstrate value to an employer rather than simply make a profit.Robert goes deep on where agentic AI is headed for both offense and defense, including a sobering implication for law enforcement; as criminal operations become increasingly automated, arresting the principals may no longer disrupt the business. His team has already put this to work on the defensive side. Their internal agentic system ACER has discovered 210 zero-days in a matter of months. He also raises a specific concern that practitioners should take seriously: CTI reports containing detailed reverse-engineering write-ups and code samples are essentially training data for malicious LLM prompting, and the industry should reconsider what level of technical detail is actually necessary to publish alongside IOCs.Topics discussed:The three-rule framework for predicting criminal adoption of emerging technologyHow the lowering technical barrier for entry shifts the entire cybercriminal bell curve upward Why embedding AI directly into malware remains rare below 1% of observed cases, and the two structural reasons that limit adoption The shift toward jailbreaking non-Western LLMs as criminal operators anticipate that law enforcement coordination is effectively nonexistentHow agentic AI transforms criminal business models from linear service stacks to exponentially scalable operations The emerging law enforcement challenge when operations are ~75% autonomous, arrests no longer constitute meaningful disruption Why CTI publishing norms need to evolve, specifically how detailed code samples and reverse-engineering screenshots in APT reports can be fed directly into LLMs to accelerate malware developmentPractical defensive posture for shadow AI proliferation: treat AI-powered tools as untrusted software under existing vulnerability management frameworksKey Takeaways: When assessing whether adversaries will adopt a new technique or tool, evaluate it through three lenses: ease of operation, return on investment versus current methods, and evolutionary fit with existing business models.Before publishing detailed reverse-engineering write-ups, code samples, or pseudocode in APT reports, assess whether that level of detail serves defender use cases or primarily serves as a development accelerant for threat actors. Audit your organization's shadow AI exposure as a software risk problem, not an AI problem. Structure specialist agents to handle discrete tasks rather than relying on a single broad LLM. Pressure-test your law enforcement response playbook against autonomous criminal infrastructure. Evaluate your AI security tooling for hallucination risk in detection workflows. Model romance scam and investment fraud at scale in your threat landscape. Monitor for jailbroken non-Western LLM wrappers in criminal marketplaces. Factor defender tooling complexity into hiring and onboarding benchmarks. Track zero-day discovery velocity as a benchmark for agentic security ROI. Listen to more episodes: Apple Spotify YouTubeWebsite

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Scott Scher on Why CTI Teams Forecast Instead of Predict

Apr 900:45:16Tap to summarize

Scott Scher, Cyber Threat Intelligence Lead, makes a distinction that reframes how intel teams should think about their own value: they are forecasters, not predictors. That shift in framing has concrete consequences for how CTI programs justify themselves internally, and Scott argues that the most meaningful metric isn't alert volume or report count, but the decisions intel has actually influenced. Scott also addresses where he sees the threat landscape heading, and his read on ransomware cuts against how many teams are still oriented. He argues that encryption-focused ransomware has largely peaked in value for attackers; the real shift is toward pure data exfiltration. He also touches on AI in CTI with a grounded take; it’s useful for accelerating manual analyst tasks like data gathering and link analysis, but only if intelligence teams define how it gets used before the organization does it for them.Topics discussed:Why CTI teams operate in the forecasting space rather than the prediction spaceThe practical implications for how assessments are communicated to stakeholders and leadershipThe challenge of quantifying CTI value through decision-driven metrics rather than output volumeMapping each stakeholder's workflow outputs and the triggers that drive them, then injecting intelligence at the right point in that chainThe evolution of ransomware toward exfiltration-only models, and why this reframes the defensive priority from backup to data loss prevention How CTI teams can use strategic intelligence to drive organizational decisions on edge device hardening and third-party riskThe role of AI in intel workflows as a force multiplier for manual analyst tasks, and why teams need to define that use case proactivelyThe collective defense model emerging at the state and local government levelWhy making analytic assessments scientifically defensible is what separates credible CTI from noiseKey Takeaways: Reframe your team's value proposition around decisions influenced, not products delivered. Map each stakeholder's workflow before defining your intelligence requirements. Conduct monthly stakeholder cadences specifically to capture feedback on delivered products. Ask stakeholders about their biggest obstacles, not just their intel requirements. Reorient ransomware defensive priorities toward data loss prevention.Use sustained trend analysis to build strategic intelligence cases for resource allocation. Get ahead of how AI is used in your CTI workflows before organizational pressure defines it for you.Treat qualitative stakeholder feedback as a scientific input, not an afterthought. Document the reasoning behind every intelligence assessment, not just the conclusion. Pursue an interdisciplinary lens when building CTI programs and hiring.

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You Can't Trust Your Zoom Call Anymore. Deepfakes, DPRK & the New Attack Surface

Mar 2600:42:30Tap to summarize

Deepfakes have moved well past the uncanny valley and into active threat operations, and Tom Cross, Head of Threat Research at GetReal, has the client-side case studies to back it up. Tom explains how North Korean IT worker infiltration campaigns have transformed HR and video conferencing from administrative functions into active attack surface, albeit one that most security teams aren't monitoring, logging, or ingesting into their SIEM.Drawing on a long-running collaboration with a former West Point professor and intelligence officer, Tom also applies the military framework of tactical, operational, and strategic intelligence to cybersecurity, arguing that most CTI programs are really just lists of burned indicators. The actual value of IOCs, he contends, is retrospective: discovering you were communicating with a known-bad actor means you may still be compromised. He makes the case for connecting adversary intent models, red team findings, and vulnerability data into a unified predictive picture. YT Thumbnail title: Your Zoom Call Is an Attack SurfaceTopics discussed:How North Korean IT worker infiltration has converted HR processes and video conferencing into an active, unmonitored attack surfaceVoice-cloned peer impersonation via messaging apps, followed by deepfaked video calls and malware deliveryWhy deepfake audio attacks on IT help desk credential reset processes are among the most likely near-term vectorsBiometric indicators of compromise and the significant false-positive risks that distinguish them from traditional IP or domain IOCsHow the military intelligence framework of tactical, operational, and strategic analysis applies to CTI programsThe strategic importance of retrospective IOC analysis versus forward-looking ingestionWhy DPRK's financial motivation model expands their target set far beyond what traditional nation-state threat modeling would predictKey Takeaways: Ingest video conferencing logs into your SIEM.Audit your remote credential reset process for social engineering resistance.Map red team findings and vulnerability data to specific adversary profiles rather than treating them as a generic remediation backlog.Implement retrospective IOC analysis alongside forward-looking blocking.Treat DPRK's financial motivation as an equalizer when assessing APT exposure.Build threat intelligence at the strategic layer by modeling adversary intent and objectives, not just cataloging observed TTPs.Apply extra care to biometric IOC sharing.Monitor employee working-hour patterns against claimed time zones as a behavioral indicator of potential employment fraud.Extend IOC taxonomy to include multimedia and biometric formats.Listen to more episodes: Apple Spotify YouTubeWebsite

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Two Minds. One Reframe. A Shift That Won't Wait.

Mar 1900:42:17Tap to summarize

Vincent Passaro, Engineering Manager at Stripe Security, didn't get there through a slide deck or a company mandate. He got there through a shower thought that followed a conversation with a friend, and it broke how he'd been thinking about building, leading, and even measuring his own team.The reframe was simple and did not start with "we're all going to be software developers. Rather, "we're going to be product owners." That single pivot changed everything downstream, including how he approached prototyping, how he set success criteria for agents, and how he coached his team out of chasing bugs and into defining outcomes.In this episode, Will and Vince trace both of their "pin drop" moments: the specific conversations that shifted their mental models, then try to articulate what that shift actually means for CTI analysts and security engineers working real problems today.They talk about what it felt like to stop asking "how do I wire this" and start asking "what does success look like," and how fast things moved once that happened. They're honest about what breaks, like the siloed tools that don't talk to each other, the governance vacuum that opens when every analyst is shipping products, and the dopamine trap of adding features instead of finishing work. And they're equally direct about what becomes possible when outcome velocity: not headcount or tooling budget, and what becomes the competitive edge.This isn't a conversation about AI hype. It's about what happens when two practitioners who've spent years operating the plumbing realize the plumbing has been commoditized and what that means for where human judgment actually matters now.If you've been waiting for the right moment to pay attention, this is probably the episode where you stop waiting.Topics Discussed"Product owner" vs. "developer" mindset and why it changes how analysts build toolingDefining outcome criteria upfront as the core discipline for AI-assisted developmentHow AI collapses experimentation costs and eliminates dev team dependencyAnalyst-owned toolkits and outcome velocity as a competitive edge for small teamsThe governance risk: product silos, duplicated tooling, and inconsistent standardsFT3 as an open-source framework built to lower the community contribution barrierWhy CISO/board resistance to AI on security grounds will backfireThreat actors are scaling the same way — analyst adaptation is the necessary responseKey Takeaways: The unlock isn't learning to code: it's learning to think backwards from the outcome. Define what success looks like, set the criteria the agent has to meet before it moves on, and stop micromanaging the implementation. That's the product owner shift.Slow down before you build. Spend more time in planning than in execution using deep research across multiple models, comparing outputs, stress-testing the concept before a single line gets written.Drop the subscription and treat the model like a teacher, not a tool. Start with a problem you already understand. Ask it to walk you from zero to fluent. It will tell you to stop thinking like a developer and start thinking like a product owner. If you have a backlog of problems you gave up on because they weren't staffable, go find them. The feasibility question that used to take months to answer now takes an afternoon. Start there.Before your next team planning cycle, map what everyone is building. The duplicate tools are already being written in parallel by people who don't know about each other. Get ahead of it now, because it only compounds.If you're involved in open-source threat intel frameworks, the contribution problem was never motivation, it was friction. The tooling gap is closable. Build the on-ramp and the community will use it.Listen to more episodes: Apple Spotify YouTubeWebsite

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TIG Risk Services' Duaine Labno on How Remote Hiring Became an Opening for Infiltration

Mar 1200:30:54Tap to summarize

What happens when a DPRK IT worker operation lands inside one of your clients, and the three-letter agency you call says they can't show up? Duaine Labno, Director of Special Investigations & Threat Intelligence at TIG Risk Services, walks through exactly that case: his team built a ruse to recover the compromised laptop, staged a physical handoff at corporate HQ, filmed the courier, ran his plates, and traced him to multiple properties. This produced the kind of ground-level intelligence the FBI told him they'd never seen before in a US-based DPRK case. Duaine explains why digital and physical investigations have to run in parallel from day one, not handed off sequentially, and what that looks like operationally when federal resources don't materialize. He also breaks down how post-COVID remote hiring processes that are speed-optimized gave adversaries a repeatable entry point, and why an untrained recruiter doing a soft document check is now a meaningful attack surface for corporate networks.YT Thumbnail title: Remote Hiring Broke Your Security PerimeterTopics discussed:How post-COVID remote hiring processes relaxed identity verification standards and created repeatable enterprise network entry points Running parallel digital and physical investigations simultaneously when tracking identity fraud and insider threatsUsing open-source intelligence and proprietary threat monitoring software to scan millions of data points for suspect behavioral patternsExecuting a live DPRK IT worker case using physical surveillance, a document ruse, and plate runs to identify a U.S.-based operatorWhy untrained recruiters conducting soft document checks have become a meaningful attack surface in corporate hiring pipelinesHow adversaries are weaponizing AI for voice alteration, deepfakes, and document manipulation to bypass hiring and KYC verification processesThe case for vetted, secure cross-industry intelligence sharing platforms to close gaps that individual organizational silos leave openWhere cyber threat intelligence trails end and physical investigation must pick up to produce actionable, court-ready evidenceKey Takeaways: Treat remote hiring pipelines as an active attack surface by pulling security, legal, and HR into the process.Train recruiters to recognize fraudulent identity documents as a first line of defense against adversarial infiltration of corporate networks.Run digital and physical investigations in parallel from the start rather than waiting for cyber analysis to conclude.Build contingency plans for federal non-response into any investigation involving foreign threat actors.Deploy threat monitoring software capable of scanning open-source data at scale to surface behavioral patterns and connections.Establish vetted, secure intelligence sharing relationships with peer organizations and law enforcement to close the visibility gaps.Pressure-test AI-assisted hiring tools against deepfake and voice alteration scenarios before deploying them.Listen to more episodes: Apple Spotify YouTubeWebsite

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Thermo Fisher's Matt McKnew on the Evolution of Ransomware as a Service

Mar 500:34:31Tap to summarize

When Matt McKnew, Senior Manager of Incident Response at Thermo Fisher, tracked down the Nimda worm in 2001 by analyzing packet captures to identify NetBIOS saturation patterns, threat actors weren't trying to get paid; they were causing disruption. Today, he's defending against ransomware groups that operate like businesses, complete with service models and affiliate networks. Matt explains why Clop's acquisition of six zero-days puts them in APT territory regardless of financial motivation, how attackers now hide in the noise of criminal operations making nation-state activity harder to detect, and why the North Korean IT worker scam succeeds by exploiting weak hiring processes rather than technical vulnerabilities. Topics discussed:Responding to the Nimda worm using packet capture analysis to identify NetBIOS saturation patterns across satellite ISP infrastructureBuilding trusted peer networks for crowdsourcing threat intelligence during active incidents rather than relying solely on formal feedsAnalyzing Clop ransomware's acquisition of six zero-days as evidence of APT-level sophistication despite purely financial motivationImplementing structured incident response documentation and processes to enable faster recovery and more nimble responseEvaluating nation-state threat actors by understanding their 5-year strategic plans and objectives rather than mapping everything to MITRE ATT&CKDeploying agentic AI to standardize analyst work products and maintain consistent intelligence delivery across global security teamsExamining North Korean IT worker infiltration campaigns that exploit weak HR and recruitment processesDifferentiating financially-motivated ransomware operations from nation-state APT campaigns while recognizing blurred lines in TTPsKey Takeaways: Document incident response procedures upfront with standardized policies to reduce response time during active security incidents.Build trusted peer networks across industry for crowdsourcing threat intelligence when formal feeds lack critical real-time information.Evaluate ransomware groups for APT-level capabilities when they acquire multiple zero-days regardless of their financial motivations.Research adversary 5-year strategic plans and national objectives to understand nation state threat actor targeting.Deploy agentic AI systems to standardize analyst work products and maintain consistent intelligence delivery formatting.Strengthen HR and recruitment processes with technical screening questions to defend against North Korean IT worker infiltration.Maintain curiosity and interrogate suspicious indicators until they make complete sense rather than accepting surface-level explanations.Recognize that attackers leverage the same automation and AI capabilities defenders use, requiring equivalent adoption to maintain defensive parity.Listen to more episodes: Apple Spotify YouTubeWebsite

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Tokio Marine HCC's Alex Bovicelli on the SMB Ransomware Wave the Industry Isn't Talking About

Feb 2600:37:15Tap to summarize

Running CTI at a cyber insurance carrier and across more than tens of thousands of companies forces a triage discipline most programs never need to build. Alex Bovicelli, Senior Director of Threat Intelligence at Tokio Marine HCC, describes how his team scaled by narrowing focus to one thing: the initial access vectors threat actors are actually using right now: not CVSS scores, not spray-and-pray alerts, but underground forum activity, access broker behavior, and credential exposure from info stealer logs that most SMBs have zero visibility into. When a detection fires, his team doesn't just notify, they walk the customer through remediation and confirm the issue is closed, because for a company relying on an MSP with no internal security staff, an alert without support is just noise.The more pointed conversation is about what's not making headlines: thousands of SMBs are getting hit by ransomware every year, and groups like Akira have built a business model specifically around it; high volume, low ransom, staying below the threshold that triggers serious law enforcement attention. Alex explains how those attacks succeed not through sophisticated tradecraft but through SSL VPN brute forcing tools left running unattended, returning thousands of valid credentials against organizations that have no account lockout policies, no MFA on remote access, and no way to know their credentials are already in a log collector somewhere. Topics discussed:Building intelligence-led CTI programs at scale by anchoring detection on initial access vectors, access broker activity, and credential exposureUsing underground forum proximity and info stealer log correlation to identify compromised credentials across thousands of organizationsOperationalizing pre-claim threat intelligence within cyber insurance to eradicate initial access before events generate claimsClosing the alert-to-remediation loop for SMBs by delivering detection, support, and mitigation confirmation as a single workflowHow Akira and similar ransomware groups deliberately target SMBs with high-volume, sub-threshold attacks Rethinking CVSS-based patching prioritization by incorporating criminal exploitability and at-scale attack frequency into triageSeparating AI as an intelligence producer from AI as a report summarizer, where automation could realistically drive patching priorityWhy most external threat feeds leave CTI teams in a retroactive posture, and how incident response data from insurance claims changes thatKey Takeaways: Anchor your CTI program on initial access vectors rather than trying to cover every vulnerability class across your environment.Monitor access broker activity and underground forums to understand which threat actors are actively buying and selling against your industry or infrastructure.Integrate info stealer log analysis into your detection pipeline to identify compromised credentials before threat actors use them for lateral movement or ransomware deployment.Shift your patching prioritization model away from CVSS scores and toward criminal exploitability.Design alerts for smaller IT teams to be remediation-ready on receipt because an alert without a clear next step will not get acted on.Close the loop on every detection by confirming mitigation was completed, not just that the alert was acknowledged.Enforce account lockout policies and MFA on all SSL VPN and remote access entry points as a baseline control.Assess AI tooling for your CTI program on whether it can produce intelligence rather than just consume it through report summarization.Use incident response data from post-claim analysis to validate your pre-claim detection signals.Listen to more episodes: Apple Spotify YouTubeWebsite

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Coalition's Daniel Woods on What Cyber Insurance Claims Reveal About Security Controls

Feb 1900:38:24Tap to summarize

Daniel Woods, Principal Security Researcher, and his team at Coalition analyzed forensic reports across their 100,000-policyholder base and found 50% of ransomware incidents begin with VPN or firewall exploits. But here's the twist: 40-60% of those aren't vulnerability exploits at all, they're stolen credentials bypassing perimeter devices entirely. Organizations running Cisco ASA devices show 5x higher claim rates than peers, with similar patterns across Fortinet, SonicWall, and Citrix SSL VPNs. When threat actors do exploit vulnerabilities, they're scanning and deploying shells within 24-48 hours of public disclosure, making your 72-hour patch SLAs dangerously obsolete.Daniel also surfaces the gap between security control theory and organizational reality. Microsoft claims 99.9% MFA effectiveness for individual Azure accounts, but insurance claims data shows no measurable risk reduction at the organizational level because that one service account without MFA, that legacy API integration nobody knew was enabled, or that exec who refused to enroll gives attackers everything they need. Organizations deploying threat-based training focused on social engineering tactics beyond phishing see measurably lower claim rates, suggesting we've been training for the wrong threat surface.Topics discussed:Analyzing cyber insurance claims data from 100,000 policyholders to identify which security controls actually reduce incident ratesUnderstanding why perimeter security devices like Cisco ASA, Fortinet, and SonicWall VPNs show 5x higher claim rates in insurance dataExamining the 40-60% of edge device breaches caused by stolen credentials rather than vulnerability exploitsClosing the gap between Microsoft's 99.9% individual MFA effectiveness claims and zero measurable organizational risk reductionRevealing security awareness training effectiveness through a study showing 2% phishing failure reduction versus threat-based training Comparing email security platforms where Google Workspace shows lower claims rates than Office365 due to included-by-default security featuresImplementing a zero-day alert service that notifies policyholders within hours when vulnerable perimeter devices need immediate patchingRethinking security awareness training as role-specific, finite courses targeting job risks rather than repetitive generic phishing exercisesKey Takeaways: Audit your external perimeter for exposed Cisco ASA, Fortinet, SonicWall, and Citrix SSL VPN devices.Implement hardware-based MFA enforcement across all services including legacy APIs and service accounts to close credential theft gaps.Reduce patch SLAs from 72 hours to under 24 hours since threat actors scan and deploy shells within 24-48 hours of vulnerability disclosure.Migrate email infrastructure to cloud-hosted platforms like Google Workspace that include security features by default.Replace repetitive generic phishing training with role-specific threat-based courses focused on social engineering tactics.Scan your policyholder or customer base for vulnerable perimeter devices using external scanning services to notify before exploits occur.Build identity management architecture around centralized services with hardware token enforcement.Evaluate security control effectiveness using multiple data sources rather than vendor claims alone.Listen to more episodes: Apple Spotify YouTubeWebsite

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Stripe's Vincent Passaro on Fraud Taxonomies & Generating Red Team Testing Roadmaps

Feb 1201:08:58Tap to summarize

Stripe's 3-person intel team created FT3 (fraud tools, tactics & techniques), a framework modeled after MITRE ATT&CK but purpose-built for financial fraud, to eliminate the communication breakdown where "fraud" required constant reverse engineering. The structured taxonomy now powers both analyst workflows and automated fraud systems operating at transaction-millisecond speeds, with technique-based tagging that gives fraud engines the context to make informed decisions without human interpretation of vague "fraudulent" alerts.Vincent Passaro, Engineering Manager at Stripe Security, walks through their shift from reactive blocking to building infrastructure targeting packages for law enforcement prosecution. By mapping card testing, account takeovers, and money movement techniques across the full attack chain, the team now produces actionable intelligence packages. The framework drives LLM-powered classification of legacy incident reports, threat-informed red team testing by automatically mapping techniques to API capabilities, and standardized intelligence sharing with financial institutions. YT Thumbnail title: Technique Tagging at ScaleTopics discussed:Creating FT3 framework modeled after MITRE ATT&CK to establish standardized fraud technique taxonomyTransitioning from AWS tier-3 incident response to financial fraud intelligence while applying cloud security methodologiesBuilding infrastructure targeting packages that map adversary infrastructure roles for law enforcement prosecutionScaling small teams through technique-based tagging that enables fraud systems to make decisions at millisecond transaction speedsLeveraging LLMs for automated classification of historical incident reports and mapping fraud techniques to API endpoint capabilitiesIntegrating threat intelligence with red team and fraud operations to create threat-informed testing roadmaps prioritized by business impactKey Takeaways: Build fraud-specific taxonomies to eliminate communication gaps where "fraud" requires constant reverse engineering.Map fraud techniques across the full attack timeline for complete adversary behavior visibility.Create infrastructure targeting packages that identify adversary server roles and network diagrams for prosecution-ready intelligence sharing.Leverage LLMs with fraud technique context to automatically classify historical incident reports and identify new techniques.Use API documentation and fraud frameworks together with LLMs to generate threat-informed red team testing roadmaps.Prioritize threat actor tracking based on business impact and platform prevalence rather than defaulting to nation-state actors or compliance checklists.Integrate threat intelligence, red team, and fraud operations under unified leadership to enable rapid validation of observed techniques.Design fraud frameworks with extensive contextual documentation to enable adoption by non-security teams and facilitate machine-readable intelligence sharing across organizations.Listen to more episodes: Apple Spotify YouTubeWebsite

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All episodes

Unit 42's Andrew Rathbun on the Sysmon Configuration Mistake Enterprises Are Making

Trend AI's Robert McArdle on Criminal Business Models Surviving Tech Revolutions

Scott Scher on Why CTI Teams Forecast Instead of Predict

You Can't Trust Your Zoom Call Anymore. Deepfakes, DPRK & the New Attack Surface

Two Minds. One Reframe. A Shift That Won't Wait.

TIG Risk Services' Duaine Labno on How Remote Hiring Became an Opening for Infiltration

Thermo Fisher's Matt McKnew on the Evolution of Ransomware as a Service

Tokio Marine HCC's Alex Bovicelli on the SMB Ransomware Wave the Industry Isn't Talking About

Coalition's Daniel Woods on What Cyber Insurance Claims Reveal About Security Controls

Stripe's Vincent Passaro on Fraud Taxonomies & Generating Red Team Testing Roadmaps