Future of Threat Intelligence

Tidal Cyber's Scott Small on Operationalizing MITRE from Intel to Validation

Tidal Cyber's Director of Cyber Threat Intelligence Scott Small reveals how his knowledge base now tracks almost 25,000 procedure-level instances across nearly 800 MITRE ATT&CK techniques and sub-techniques, capturing the command-level detail that exposes the false promise of "100% coverage" when working at technique abstraction alone. He argues that the pre-attack reconnaissance phase remains the most essential yet most ignored portion of the framework, including the recently formalized technique for purchasing and selling victim data on stealer marketplaces. 
Scott's AI workflow treats LLMs strictly as structured data processors that reference MITRE's written technique examples to parse unstructured threat reports, refusing to use them as intelligence sources themselves. He's seeing threat intelligence and detection engineering roles merge as individuals develop hybrid skill sets. His methodology for mapping TTPs to vulnerabilities gives security teams a data-driven rationale to deprioritize patches when strong post-exploitation defenses already cover the attack vector.
Topics discussed:
Tracking almost 25,000 procedure-level instances across 800 MITRE ATT&CK techniques to expose the false promise of technique-level coverage alone
Defending pre-attack reconnaissance phases including the technique for purchasing victim data on stealer marketplaces
Classifying scanning activity by threat type to prioritize C2 infrastructure linked to APTs over fraud-related domains
Blending threat intelligence and detection engineering roles as analysts gain EDR skills 
Using AI as structured data processors that reference MITRE's written technique examples to parse unstructured threat reports without generating intelligence
Mapping TTPs to vulnerabilities to create data-driven rationale for deprioritizing patches when post-exploitation defenses cover the vector
Visualizing attack narratives through the MITRE ATT&CK matrix to tell leadership about defense gaps and justify resource allocation decisions
Key Takeaways: 
Track adversary procedures at the command and protocol level to identify real defense gaps.
Monitor stealer marketplace activity and automated dealer platforms for credential exposures tied to your domain, then reset credentials.
Prioritize threat intel alerts by focusing first on APT-linked activity over fraud campaigns.
Develop hybrid skill sets where CTI analysts understand EDR logging capabilities and threat hunters consistently consult adversary behavior reporting for hunt hypotheses.
Implement AI workflows that use LLMs to extract structured technique data from unstructured threat reports, not as intelligence output itself.
Map TTPs to specific vulnerabilities to build data-driven cases for deprioritizing patches when post-exploit defenses provide coverage.
Create visual attack narratives using the MITRE ATT&CK matrix to communicate defense gaps and resource needs.
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Marsh McLennan's Casey Beaumont on Vendor Breach Assessments That Cut through Legal Games

When Casey Beaumont's entire CTI team departed just before new analysts started, she found herself running threat intelligence solo for months while directing incident response, threat hunting, and red team operations. That trial by fire taught her exactly what separates tactical intelligence from strategic value, and why the best analysts invest significant personal time building trust networks that enterprise tools cannot replicate.
Casey's teams at Marsh McLennan, where she’s the Director of Advanced Cyber Practices, received warnings about Scattered Spider infrastructure 20 minutes after domains registered, before threat actors sent a single SMS phishing message to employee cell phones. That early intelligence enabled blocking domains internally and preparing communications before the first report came in. These private intel networks, built through years of trust and after-hours engagement, consistently deliver the warnings that matter most for large enterprises facing sophisticated, targeted attacks.
 
Beyond tactical response, Casey explains how her CTI program produces strategic intelligence that drives architectural decisions. She also shares her framework for vendor breach assessments that cuts through legal wordplay, why attribution matters far less than response speed during active incidents, and how to scope CTI mission appropriately to prevent analyst burnout in organizations with massive attack surfaces.
 
Topics discussed:
Managing unified teams of CTI, threat hunting, red team, and incident response to eliminate resource allocation friction during active incidents and supply chain events.
Building private intelligence networks that deliver infrastructure warnings within 20 minutes of threat actor activity.
Transitioning from tactical incident response to strategic CTI leadership and learning analyst tradecraft through necessity when running solo.
Conducting vendor breach assessments using four critical questions about control gaps, persistence, data exposure, and remediation plans.
Evaluating intelligence relevance at large enterprises with complex environments where shadow IT, acquisitions, and distributed technology create unclear exposure.
Why vendor breaches should not automatically disqualify partnerships and how strong vendor relationships enable influence over authentication improvements and security controls.
Producing strategic CTI that drives architectural investment decisions by documenting systemic risks across technology ecosystems rather than isolated incidents.
Understanding CTI stakeholder needs through deliberate interviewing to prevent analysts from producing reports that leadership ignores.
Sharing unattributed intelligence with law enforcement that enabled warnings to seven or eight fully breached companies with no awareness of compromise.
Why leadership overemphasizes attribution during active incidents when tactical response and containment should take priority.
How great CTI analysts invest significant personal time building professional brands, attending conferences, and earning trust in private intelligence communities.
Key Takeaways: 
Consolidate CTI, threat hunting, red team, and incident response under unified leadership to eliminate resource allocation friction during active supply chain incidents and targeted attacks.
Conduct vendor breach assessments using four critical questions: what control gaps enabled the breach, does the actor maintain persistence, what client data was exposed, and what remediation plans address root causes.
Identify vendor evasiveness during breach discussions by listening for careful language around product names that insinuate limited scope while obscuring broader organizational compromise.
Produce strategic CTI reports that document systemic risks across technology ecosystems rather than isolated incidents to give executives justification for architectural investment decisions.
Interview CTI stakeholders systematically to understand what intelligence formats and content they need before analysts waste time producing reports that leadership ignores.
Scope CTI team mission to specific focus areas like tactical threats and supply chain rather than attempting comprehensive coverage of vulnerabilities, geopolitics, and fraud with limited staff.
Share unattributed threat intelligence with law enforcement partners when legal and privacy teams approve to enable warnings for other breached organizations unaware of compromise.
Deprioritize threat actor attribution during active incident response unless conclusive evidence enables tactical pivots, focusing instead on containment and remediation before forensic analysis.
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State CISOs on Why Cyberattacks Against 1 State Attack All of America

Michael Moore, CISO for the Secretary of State of Arizona's office, explains how he acts as a virtual CISO for all 15 counties by conducting physical security assessments at election facilities and providing real-time guidance during critical events. His approach treats surprise attacks as learning opportunities that should only work once, immediately sharing adversary infrastructure and TTPs across the entire election community to burn their capabilities. Michael emphasizes that misinformation, disinformation, and malinformation represent converging threat vectors that manifest as both cyber attacks and physical violence, requiring defenders to think beyond traditional security boundaries.
Ryan Murray, CISO for the State of Arizona, shares his Cybersecurity Trinity for AI framework: defend from AI-enabled attacks, defend with AI-augmented tools, and defend the AI systems organizations deploy. He explains how Arizona replicated MS-ISAC functionality through AZ ISAC, enabling 1,000+ government personnel across 200+ entities to share intelligence in real time without requiring mature security programs. Ryan stresses that organizations already generate valuable threat intelligence internally through phishing reports and security alerts, and the real challenge is communication and relationship-building rather than expensive commercial feeds.
Topics discussed:
How physical security gaps at government facilities create tactical vulnerabilities that scale across entire states.
Building sector champion models where election security and critical infrastructure specialists act as virtual CISOs for under-resourced local governments.
Why misinformation, disinformation, and malinformation represent converging cyber, physical, and reputational threat vectors that radicalize populations into kinetic attacks.
Implementing real-time threat intelligence sharing protocols that enable 1,000+ defenders to communicate via platforms like Slack during active incidents.
The evolution from receiving threat intelligence to generating intelligence internally by analyzing phishing campaigns, user reports, and infrastructure scanning patterns.
Applying the "surprise attack only works once" principle by burning adversary infrastructure and TTPs immediately through broad intelligence sharing.
Why the distinction between "intelligence" in national security contexts versus cyber threat intelligence creates executive buy-in challenges.
How to prove negative outcomes and communicate near-miss stories where intelligence prevented catastrophic breaches.
The collapsing patch window problem where automated vulnerability discovery and exploitation eliminates traditional seven-day remediation timelines.
Implementing the Cybersecurity Trinity for AI: defending from AI-enabled attacks, defending with AI-enhanced tools, and defending AI systems from prompt injection and data leakage.
Why secure-by-design pledges fail when financially motivated vendors push defensive responsibility to the least capable organizations.
Building tabletop exercise programs that prepare election officials for denial-of-service attacks disguised as physical threats.
How generative AI enables Script Kitty 2.0, where non-technical adversaries automate reconnaissance, exploitation, and data exfiltration through natural language prompts.
The challenge of deepfakes and synthetic media targeting sub-national officials who lack the visibility and resources for sophisticated reputation defense.
Key Takeaways: 
Build sector champion programs where specialists act as virtual CISOs for under-resourced entities.
Implement real-time communication platforms like Slack that enable defenders to share threat indicators during active incidents.
Generate internal threat intelligence by systematically analyzing phishing campaigns, tracking top recipients, subject lines, and infrastructure patterns.
Apply the principle that surprise attacks should only work once by immediately burning adversary infrastructure and TTPs through broad community sharing.
Use tabletop exercises to prepare personnel for converged threats like bomb hoaxes that function as denial-of-service attacks on critical operations.
Frame AI strategy using the Cybersecurity Trinity: defend from AI-enabled attacks, defend with AI tools, and defend AI systems from exploitation.
Recognize that patch windows have collapsed to zero for critical edge-facing vulnerabilities due to automated discovery and weaponization.
Focus communications on near-miss stories that demonstrate how intelligence prevented catastrophic outcomes before executive awareness.
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Safebooks AI’s Ahikam Kaufman on Why CFOs Need Company-Specific AI Models for Fraud Detection

Unlike CISOs who work with consistent vulnerabilities across cloud environments, CFOs face company-specific financial processes that change constantly, making automation historically complex to solve before the AI era. Ahikam Kaufman, CEO & CFO of Safebooks AI, explains why machine learning is the only viable solution to detect sophisticated embezzlement schemes that regulatory compliance demands every public company address — with no materiality threshold. 
His background building fraud prevention systems at Intuit and Check has taught him how graph technology can link seemingly unrelated financial transactions to expose coordinated internal fraud attempts that would be impossible for humans to catch at scale. The challenge is compounded by the fact that most finance staff are accountants, not technologists, requiring AI tools that bridge data complexity without demanding high technical skill levels.
Topics discussed:
Sarbanes-Oxley requires fraud protection programs with no materiality thresholds, yet most organizations lack systematic detection across payroll, vendor, and expense systems.
Financial fraud detection requires unique AI models for each company using historical data, unlike consistent threats across organizations.
Advanced fraud schemes link multiple transaction types requiring graph technology to connect disparate activities that individual monitoring would miss.
Fraudsters use AI for parallel attacks, fake invoices, vendor manipulation, and executive impersonation, requiring automated defense systems for real-time processing.
Achieving 99.9% accuracy through structured enterprise data and rule-based controls where financial precision is non-negotiable.
Financial AI platforms integrate with existing systems without replacements or workflow changes, providing immediate automation value.
Key Takeaways: 
Implement AI-powered fraud detection systems that monitor vendor account changes, payroll additions, and journal entry anomalies.
Build company-specific AI models using 1-2 years of historical financial data to learn unique business processes, data structures, and transaction patterns.
Deploy graph technology to link related financial transactions across different systems to identify coordinated fraud attempts.
Establish partnerships between CFOs and CISOs to combine external cybersecurity threat detection with internal financial fraud monitoring.
Focus on AI platforms that integrate with existing financial technology stacks without requiring system replacements.
Create rule-based governance frameworks for financial AI systems to eliminate hallucinations and maintain accuracy levels.
Monitor AI-amplified fraud techniques, such as sophisticated fake invoices, manipulated vendor banking information, and executive impersonation.
Develop automated systems that can demonstrate reasonable effort for fraud prevention to satisfy regulatory requirements and insurance protections.
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Marsh's Sjaak Schouteren on the Golden Rule of Risk Assessment

Cyber insurance has transformed from a liability-focused niche product into a comprehensive business continuity tool, but widespread misconceptions continue to prevent organizations from maximizing its strategic value. Sjaak Schouteren, Cyber Growth Leader - Europe at Marsh, offers David how they combine risk quantification with business-focused communication strategies that give security leaders the tools to speak board language about cyber threats.
Rather than the complex audit processes, modern cyber insurance acquisition can be remarkably streamlined. Sjaak's experience managing real-world incident response highlights how proper coverage creates strategic advantages beyond simple risk transfer, including immediate access to specialized negotiation teams and forensics experts who can extend decision timeframes during crisis situations.
Topics discussed:
How the 2020-2022 ransomware surge taught insurers that mid-cap companies were primary targets requiring comprehensive coverage.
The three-pillar structure of modern cyber insurance covering first-party losses, third-party liability, and immediate incident response services without deductibles for initial crisis management.
Why risk quantification through scenario analysis and financial impact modeling provides CISOs with the business language needed to communicate effectively with boards and C-suite executives.
How risk engineers from security backgrounds have eliminated technical translation barriers between IT teams and underwriters.
The strategic advantage of immediate incident response coverage that provides access to specialized forensics, legal, and negotiation teams within 48-72 hours of an incident.
Why organizations with cyber insurance actually pay ransomware demands less frequently due to professional negotiation teams and comprehensive recovery support.
The evolution from narrow data breach coverage to comprehensive business protection across all organization sizes.
The distinction between risk mitigation through security controls and risk transfer through insurance as complementary rather than competing strategies.
Key Takeaways: 
Conduct cross-functional scenario planning to identify business-critical cyber risks before evaluating insurance coverage options.
Map potential cyber incidents on a risk heat map measuring probability and impact to distinguish between minor inconveniences and threats that could damage business operations.
Quantify average and maximum financial losses for each business-critical scenario to make data-driven decisions about risk.
Leverage specialized risk engineers from security backgrounds during the underwriting process to eliminate technical translation barriers.
Engage professional ransomware negotiators rather than attempting internal negotiations.
Position cyber insurance as business enablement rather than just risk transfer by demonstrating how coverage strengthens overall cyber resilience.
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SIG's Rob van der Veer on Why "Starting Small" with AI Security Might Fail

What happens when someone who's been building AI systems for 33 years confronts the security chaos of today's AI boom? Rob van der Veer, Chief AI Officer at Software Improvement Group (SIG), spotlights how organizations are making critical mistakes by starting small with AI security — exactly the opposite of what they should do.
From his early work with law enforcement AI systems to becoming a key architect of ISO 5338 and the OWASP AI Security project, Rob exposes the gap between how AI teams operate and what production systems actually need. His insights on trigger data poisoning attacks and why AI security incidents are harder to detect than traditional breaches offer a sobering reality check for any organization rushing into AI adoption.
The counterintuitive solution? Building comprehensive AI threat assessment frameworks that map the full attack surface before focused implementation. While most organizations instinctively try to minimize complexity by starting small, Rob argues this approach creates dangerous blind spots that leave critical vulnerabilities unaddressed until it's too late.
Topics discussed:
Building comprehensive AI threat assessment frameworks that map the full attack surface before focused implementation, avoiding the dangerous "start small" security approach.
Implementing trigger data poisoning attack detection systems that identify backdoor behaviors embedded in training data.
Addressing the AI team engineering gap through software development lifecycle integration, requiring architecture documentation and automated testing before production deployment.
Adopting ISO 5338 AI lifecycle framework as an extension of existing software processes rather than creating isolated AI development workflows.
Establishing supply chain security controls for third-party AI models and datasets, including provenance verification and integrity validation of external components.
Configuring cloud AI service hardening through security-first provider evaluation, proper licensing selection, and rate limiting implementation for attack prevention.
Creating AI governance structures that enable innovation through clear boundaries rather than restrictive bureaucracy.
Developing organizational AI literacy programs tailored to specific business contexts, regulatory requirements, and risk profiles for comprehensive readiness assessment.
Managing AI development environment security with production-grade controls due to real training data exposure, unlike traditional synthetic development data.
Building "I don't know" culture in AI expertise to combat dangerous false confidence and encourage systematic knowledge-seeking over fabricated answers.
 
Key Takeaways: 
 
Don't start small with AI security scope — map the full threat landscape for your specific context, then focus implementation efforts strategically.
Use systematic threat modeling to identify AI-specific attack vectors like input manipulation, model theft, and training data reconstruction.
Create processes to verify provenance and integrity of third-party models and datasets.
Require architecture documentation, automated testing, and code review processes before AI systems move from research to production environments.
Treat AI development environments as critical assets since they contain real training data.
Review provider terms carefully, implement proper hardening configurations, and use appropriate licensing to mitigate data exposure risks.
Create clear boundaries and guardrails that actually increase team freedom to experiment rather than creating restrictive bureaucracy.
Implement ongoing validation that goes beyond standard test sets to detect potential backdoor behaviors embedded in training data.
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Vigilocity's Karim Hijazi on Supply Chain Threat Intelligence

Karim Hijazi’s approach to threat hunting challenges conventional wisdom about endpoint security by proving that some of the most critical intelligence exists outside organizational networks. As Founder & CEO of Vigilocity, his 30-year journey from the legendary Mariposa botnet investigation to building external monitoring capabilities demonstrates why DNS analysis remains foundational to modern threat detection, even as AI transforms both offensive and defensive capabilities.
In his chat with David, Karim explores how threat actors continue to rely on command and control infrastructure as their operational lifeline. His insights into supply chain threats, "low and slow" reconnaissance campaigns, and the evolution of domain generation algorithms provide security leaders with a unique perspective on proactive defense strategies that complement traditional security controls.
Topics discussed:
External DNS monitoring approaches that identify threat actor infrastructure before weaponization.
How AI has fundamentally disrupted domain generation algorithm prediction, creating new blind spots for traditional threat intelligence.
Supply chain threat intelligence methodologies that identify compromised partners and assess contagion risks.
The evolution of command and control infrastructure from cleartext to encrypted communications and back.
"Low and slow" reconnaissance patterns that precede ransomware attacks, operating with months-long dormancy periods.
Strategies for communicating threat intelligence value to business stakeholders without creating defensive reactions from security teams.
The limitations of current AI applications in security, particularly around nuanced threat analysis requiring human experience and pattern recognition.
Board-level cybersecurity education requirements for organizations to survive sophisticated attacks in the next 5 years.
Innovation challenges in cybersecurity where rebranding existing solutions prevents breakthrough defensive capabilities.
Non-invasive threat hunting philosophies that deliver forensic-level detail without deploying endpoint agents.
Key Takeaways: 
Monitor external DNS communications to identify command and control infrastructure before threat actors weaponize domains against your organization.
Assess supply chain partners through external threat intelligence lenses to identify compromised third parties that represent contagion risks.
Develop detection capabilities for "low and slow" reconnaissance campaigns that operate with extended dormancy periods between communications.
Implement AI as a noise reduction tool rather than a primary decision maker, maintaining human oversight for nuanced threat analysis.
Establish board-level cybersecurity expertise to ensure adequate understanding and support for advanced threat hunting investments.
Focus security innovation efforts on breakthrough capabilities rather than rebranding existing solutions with new acronyms.
Correlate external threat intelligence with internal security data to validate threats and reduce false positive rates.
Build threat hunting capabilities that can operate at machine speeds to handle increasing volumes of AI-generated attacks.
Create communication strategies that present external threat intelligence as validation tools rather than indictments of existing security programs.
Maintain expertise in DNS analysis and network fundamentals as core competencies, regardless of technological advances.
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CyberHoot's Craig Taylor on Why Fear-Based Phishing Training Fails

Psychology beats punishment when building human firewalls. Craig Taylor, CEO & Co-founder of CyberHoot, brings 30 years of cybersecurity experience and a psychology background to challenge the industry's fear-based training approach. His methodology replaces "gotcha" phishing simulations with positive reinforcement systems that teach users to identify threats through skill-building rather than intimidation.
Craig also touches on how cybersecurity is only 25 years old compared to other fields, like medicine's centuries of development, leading to significant industry mistakes. NIST's 2003 password requirements, for example, were completely wrong and took 14 years to officially retract. Craig's multidisciplinary approach combines psychology with security practice, recognizing that the industry's single-focus mindset contributed to these fundamental errors that organizations are still correcting today.
Topics discussed:
Replacing fear-based phishing training with positive reinforcement systems that teach threat identification through skill-building.
Implementing seven-point email evaluation frameworks covering sender domain verification, emotional manipulation detection, and alternative communication verification protocols.
Developing 3- to 5-minute gamified training modules that reward correct threat identification across specific categories.
Correcting cybersecurity industry misconceptions through multidisciplinary approaches.
Evaluating emerging security technologies like passkeys through industry backing analysis.
Building human firewall capabilities through psychological understanding of manipulation tactics.
Implementing pause-and-verify protocols to confirm unusual requests that pass technical email verification checks.
Key Takeaways: 
Replace punishment-based phishing simulations with positive reinforcement training that rewards users for correctly identifying threat indicators.
Implement gamified security training modules instead of lengthy video sessions to maintain user engagement.
Establish pause-and-verify protocols requiring alternative communication channels to confirm unusual requests that pass technical email verification checks.
Evaluate emerging security technologies by examining industry backing and major sponsor adoption before incorporating them into training programs.
Calibrate reward systems to provide minimal incentives (like monthly lunch gift cards) that drive engagement without creating external dependency.
Train users to identify the seven key phishing indicators: sender domain accuracy, suspicious subject lines, inappropriate greetings, poor grammar, external links, questionable attachments, and emotional urgency tactics.
Build internal locus of control in security training by focusing on skill mastery rather than fear-based compliance, ensuring users understand why security practices protect them personally.
Deploy fully automated security training systems that eliminate administrative overhead while maintaining month-to-month flexibility and offering discounts to educational and nonprofit organizations.
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The Futurum Group's Fernando Montenegro on the OODA Loop Approach to Security Strategy

What happens when you apply economic principles like opportunity cost and comparative advantage to cybersecurity decision-making? Fernando Montenegro, VP & Practice Lead of Cybersecurity at The Futurum Group, demonstrates how viewing security through an economics lens reveals critical blind spots most practitioners miss. His approach transforms how organizations evaluate cloud migrations, measure program success, and allocate security resources.
Fernando also explains why cybersecurity has evolved from a technical discipline into a socioeconomic challenge affecting society at large. His three-part framework for AI implementation — understanding the technology, mapping business needs, and assessing threat environments — offers security leaders a structured approach to cutting through hype and making strategic decisions. 
Topics discussed:
How security economics and opportunity cost analysis reshape cloud migration decisions and resource allocation strategies
The National Academies' 2025 "Cyber Hard Problems" report and its implications for cybersecurity's expanding societal impact
A three-part framework for AI implementation: technology comprehension, business alignment, and threat environment assessment
Why understanding organizational business operations eliminates the biggest blind spot in threat intelligence programs
Multi-layered professional networking strategies for separating signal from noise in threat intelligence analysis
How cloud environments fundamentally change threat intelligence workflows from IP-based to identity and architecture-focused approaches
Key Takeaways: 
Apply economic opportunity cost analysis to security decisions by evaluating what you give up versus what you gain from each security investment.
Map your organization's business operations across marketing, sales, and product development to provide crucial context for technical threat intelligence.
Assess AI implementations through a three-part framework: technology limitations, business use cases, and specific threat considerations.
Measure security program success by evaluating alignment with organizational goals and influence on non-security business decisions.
Run intentional OODA loops on your security program to maintain strategic direction and continuous improvement.
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T. Rowe Price’s PJ Asghari’s "What, So What, Now What" Framework for Threat Intel

What does it take to transform a traditional event-driven SOC into an intelligence-driven operation that actually moves the needle? At T. Rowe Price, it meant abandoning the "spray and pray" approach to threat detection and building a systematic framework that prioritizes threats based on actual business risk rather than industry hype.
PJ Asghari, Team Lead for Cyber Threat Intelligence Team, walked David through their evolution from a one-person intel operation to a program that directly influences detection engineering, fraud prevention, and executive decision-making. His approach centers on the "what, so what, now what" framework for intelligence reporting — a simple but powerful structure that bridges the gap between technical analysis and business action.
Topics discussed:
Moving beyond event-based monitoring to prioritize threats based on sector-specific risk profiles and threat actor targeting patterns rather than generic threat feeds.
Focusing on financially-motivated actors, initial access brokers, and PII theft rather than nation-state activities that rarely target mid-tier financial firms directly.
Addressing the cross-functional challenge that spans HR, talent acquisition, insider threat, and CTI teams.
Using mise en place principles from culinary backgrounds to establish clear PIRs that align team focus with organizational needs.
Creating trackable deliverables through ticket systems, RFI responses, and cross-team support that translates intelligence work into measurable business impact.
Maintaining critical thinking and media literacy skills while leveraging automation for administrative tasks and threat feed processing.
Key Takeaways: 
Implement the "what, so what, now what" reporting structure to ensure intelligence reaches appropriate audiences with clear business implications and recommended actions.
Build cross-functional relationships with fraud, insider threat, and vulnerability management teams to create measurable value through ticket creation and support requests rather than standalone reporting.
Establish sector-specific threat prioritization by mapping threat actors to your actual business model rather than following generic industry threat landscapes.
Create trackable metrics through service delivery, including RFI responses, expedited patching recommendations, and credential compromise notifications to demonstrate concrete value.
Focus hiring on inquisitive mindset and communication skills over certifications, using interviews to assess critical thinking and ability to dig deeper into investigations.
Map threat actor TTPs to MITRE framework to identify defense stack gaps and provide actionable detection engineering guidance rather than just IOC sharing.
Invest in dark web monitoring and external attack surface management for financial services to catch credential compromises and brand abuse before they impact customers.
Establish regular threat actor recalibration cycles to ensure prioritization remains aligned with current threat landscape rather than outdated assumptions.
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