Balance the Triangle Daily Brief — 2026-02-16
Technology is moving faster than society is adapting.
Today’s ownership tension: India hosts the first Global AI Impact Summit in the Global South starting today, with OpenAI reporting 100 million weekly active users in the country—the second largest user base globally and the largest student user base worldwide. CISA’s February 16 remediation deadline expires today for BeyondTrust Privileged Remote Access, SolarWinds Web Help Desk, and Microsoft Office vulnerabilities already exploited in active cyberattacks against federal agencies and enterprises. North Korea’s state-backed hacking group UNC2970 is using Google Gemini to synthesize open-source intelligence, profile high-value targets at cybersecurity and defense companies, and map technical job roles for campaign planning. AI adoption is scaling to hundreds of millions of users faster than governance frameworks can adapt, known vulnerabilities remain unpatched past compliance deadlines, and nation-state actors are weaponizing the same enterprise AI tools organizations deploy for productivity.
Why This Matters Today
We’re witnessing three simultaneous failures in operational technology governance: AI adoption is scaling from thousands of pilot users to hundreds of millions of production users faster than data residency, model safety, and accountability frameworks can adapt to mass deployment. Compliance remediation deadlines are being met through documentation and exceptions rather than actual patching, leaving organizations “compliant” on paper but vulnerable in practice. And adversaries are using legitimate enterprise AI tools—the same ones your employees use for research and productivity—to conduct reconnaissance, profile targets, and plan campaigns at speeds and scales that human defenders can’t match.
India’s 100 million weekly active OpenAI users signals a fundamental shift: AI is no longer an enterprise productivity experiment confined to controlled pilot programs. It’s a national strategic asset being deployed at population scale. When AI scales from 1,000 users to 100,000 to 100 million, governance frameworks designed for pilot programs break. Data residency requirements built for centralized control fail when models process data across borders. Model safety testing designed for limited use cases can’t predict emergent behaviors at scale. Accountability mechanisms that assign individual responsibility collapse when millions of users generate billions of AI interactions daily.
CISA’s Known Exploited Vulnerabilities catalog with February 16 remediation deadlines exposes the gap between compliance theater and actual security posture. Organizations validate vulnerabilities exist, assess business impact, request change approval, schedule maintenance windows, document exceptions for systems that can’t be patched immediately, and file compliance reports showing they “met the deadline”—while vulnerable systems remain exposed to active exploitation. Compliance measures policy adherence, not risk reduction. Meeting deadlines through documentation doesn’t prevent breaches.
North Korea using Google Gemini for cyber reconnaissance proves that enterprise AI tools designed for legitimate research enable adversarial operations at scale. Attackers don’t need custom reconnaissance tools when they can use Gemini, ChatGPT, or Claude to synthesize public information about target organizations, identify high-value employees and their technical roles, map organizational structures and reporting relationships, and generate detailed profiles that inform spearphishing campaigns and social engineering attacks. The same AI capabilities that make your employees more productive make attackers more effective.
The organizations managing these challenges effectively aren’t just working harder—they’re building governance architectures that scale to mass adoption, treating compliance deadlines as operational triggers rather than bureaucratic checkboxes, and assuming adversaries use AI for reconnaissance against them. They’re measuring governance by outcomes (AI incidents per million interactions, bias detection rates, hallucination containment speed) not inputs (policies written, training completed, audits conducted). They’re patching critical systems within 48-72 hours of CISA KEV additions regardless of compliance timelines. And they’re limiting publicly available information that attackers can feed to AI while monitoring for reconnaissance indicators that suggest targeting activity.
At a Glance
• India’s Global AI Impact Summit begins today in Delhi—first global AI summit hosted in the Global South, with OpenAI reporting 100 million weekly active users in India (second largest globally, largest student base worldwide). • CISA’s February 16 remediation deadline expires for BeyondTrust Privileged Remote Access (CVE-2026-1731), SolarWinds Web Help Desk, and Microsoft Office vulnerabilities already exploited in attacks against federal agencies. • North Korea’s UNC2970 threat group using Google Gemini to synthesize OSINT, profile high-value targets at cybersecurity and defense companies, map technical job roles and salary information for spearphishing campaign planning.
Story 1 — AI Adoption Outpaces Governance Capacity
What Happened
The Global AI Impact Summit opened today in New Delhi, India, marking the first major international AI policy summit hosted in the Global South. OpenAI CEO Sam Altman announced during the summit that India now has 100 million weekly active users of OpenAI’s products—making it the second largest user base globally after the United States and the largest student user base worldwide.
The announcement underscores India’s rapid AI adoption trajectory. The country has positioned itself as a “full-stack AI leader” with significant investment in AI research, development, and deployment across education, healthcare, government services, and enterprise operations. Major Indian technology companies and startups are integrating AI capabilities at scale, universities are deploying AI-powered educational tools to millions of students, and government agencies are piloting AI-assisted public services.
The summit brings together government officials, technology leaders, researchers, and civil society organizations from across Asia, Africa, and Latin America to discuss AI governance frameworks appropriate for developing economies, data sovereignty and cross-border data flows, ethical AI deployment in diverse cultural contexts, workforce transformation and skills development, and infrastructure requirements for AI at scale.
India’s 100 million weekly active users represents more than just adoption metrics—it signals that AI deployment has crossed the threshold from enterprise pilot programs to population-scale infrastructure. For context, many individual enterprises consider deployments to 10,000-50,000 users to be “large scale.” India has reached 100 million weekly active users in approximately 18-24 months, a pace of adoption that existing governance frameworks weren’t designed to accommodate.
Why It Matters
AI governance frameworks—data residency requirements, model safety protocols, bias detection and mitigation, accountability mechanisms, incident response procedures—were developed during an era when AI deployment was measured in thousands of users conducting tens of thousands of interactions. These frameworks assume:
Centralized control: AI models operate in controlled environments where access can be monitored, inputs can be validated, and outputs can be reviewed before deployment.
Predictable use cases: AI is deployed for specific, well-defined tasks where expected behaviors can be mapped in advance and anomalies can be detected through deviation from baseline.
Individual accountability: AI-generated outputs can be traced to specific users who bear responsibility for verifying accuracy and appropriateness before acting on AI recommendations.
Manageable incident scope: When AI failures occur—hallucinations, bias, inappropriate outputs—they affect limited populations and can be remediated through targeted interventions.
These assumptions break at 100 million weekly active users generating potentially billions of AI interactions:
Centralized control becomes impossible: Models operate across distributed infrastructure, users access from diverse geographic locations with varying data protection requirements, inputs come from sources that can’t all be validated, and outputs are deployed at speeds that preclude pre-release human review.
Use cases become unpredictable: Users discover creative applications the model wasn’t designed for, edge cases emerge that weren’t considered during safety testing, interactions between users create emergent behaviors that individual testing didn’t predict, and the sheer volume of usage reveals failure modes that low-scale testing missed.
Individual accountability collapses: Millions of users generate more AI outputs than can be individually traced and verified, determining responsibility for AI-generated errors becomes computationally infeasible, and users develop over-reliance where AI outputs are trusted without verification because verification at scale is impractical.
Incident scope becomes unmanageable: AI failures that affect even 0.1% of 100 million users means 100,000 people experience problematic outputs, remediation requires coordinated action across massive user bases, and the speed of AI deployment means harmful outputs can propagate before detection occurs.
The result: governance frameworks designed for 1,000 users fail to protect 100 million users. Organizations discover that policies written for controlled pilots don’t prevent failures at population scale.
Operational Exposure
If your organization is deploying AI to large user bases—tens of thousands, hundreds of thousands, or millions of users—and your governance framework was designed for pilot programs with hundreds or thousands of users, you’re operating with controls that don’t match your risk exposure.
This affects:
Data governance: AI models processing data from millions of users across multiple jurisdictions create data residency compliance exposure (GDPR, data localization requirements, cross-border transfer restrictions). Pilot program controls that assumed data stays within single jurisdiction fail when scale requires distributed infrastructure.
Model safety: AI safety testing conducted on limited use cases misses failure modes that emerge only at scale. Bias that affects 0.1% of test cases becomes 100,000 affected users at 100 million scale. Hallucinations that occur rarely in testing become daily incidents in production.
Accountability: Determining responsibility for AI-generated outputs becomes computationally and organizationally infeasible at scale. If AI generates problematic content, identifying which user prompted it, what context led to the output, and who should have caught the error before deployment requires tracing through billions of interactions.
Incident response: AI failures at scale require coordinated response across massive user bases. Traditional incident response—identify issue, contain exposure, remediate affected systems, communicate with stakeholders—assumes manageable scope. At 100 million users, even well-executed response can’t reach all affected parties before harm propagates.
Who’s Winning
One global technology company deployed AI-powered code completion tools to 80,000 software engineers across 25 countries in Q4 2025. They recognized that governance frameworks designed for their initial 500-user pilot wouldn’t scale. Their approach:
Phase 1 (Weeks 1-4): Built governance that scales with automation
Traditional governance approach: human review of AI outputs, manual audits of AI interactions, individual accountability for verification. This works for 500 users generating 10,000 daily interactions. It fails for 80,000 users generating millions.
They implemented automated governance: Real-time monitoring of AI code suggestions with automated scanning for security vulnerabilities, license violations, and credential exposure. Anomaly detection flagging unusual patterns (AI suggesting code that accesses sensitive APIs without typical authentication, AI generating code patterns associated with known vulnerabilities). Automated blocking of high-risk outputs before they reach users (AI suggestions containing hard-coded credentials, AI-generated code with SQL injection patterns, suggestions that violate security policies).
Tiered access controls based on data sensitivity: Junior engineers using AI for general coding tasks (low-risk, minimal restrictions). Senior engineers accessing proprietary algorithms (medium-risk, additional monitoring). Engineers working on security-critical systems (high-risk, AI suggestions limited to syntax and refactoring, no architecture or logic suggestions that could introduce vulnerabilities).
Result: Governance monitoring scaled from 500 users to 80,000 users without proportional increase in human governance staff. Automated controls caught and blocked 847 high-risk AI suggestions in first quarter (credential exposure, vulnerability patterns, license violations) before they reached engineers.
Phase 2 (Weeks 5-8): Implemented incident response for mass deployment
Traditional incident response assumes limited scope: AI produces problematic output, identify affected users, notify and remediate. At scale, even 0.1% incident rate affects 80 engineers—too many for individual remediation.
They built scalable incident response: Automated detection of AI output patterns associated with incidents (sudden spike in security vulnerability suggestions, cluster of similar inappropriate outputs, anomalous code generation patterns). Mass notification systems that could reach all 80,000 engineers within minutes if critical AI failure detected. Automated rollback capability: if AI model produces systematically problematic outputs, disable model, roll back to previous version, notify users, investigate root cause.
Runbooks for different incident types: AI model hallucination (generating non-existent APIs), AI model bias (suggesting code patterns that disadvantage specific user populations), AI security failure (suggesting vulnerable code at scale), AI compliance violation (generating code that violates data protection requirements).
Result: When AI model generated code suggestions with subtle memory leak pattern affecting approximately 200 engineers over 3-day period, automated detection flagged anomaly within 4 hours of first occurrence, mass notification reached all engineers within 30 minutes with guidance to review recent AI suggestions for specific pattern, automated tooling scanned code repositories to identify which engineers had accepted problematic suggestions, and team provided targeted remediation to affected engineers within 48 hours. Total engineers affected: 200. Engineers who deployed problematic code to production: 0 (caught before deployment).
Phase 3 (Weeks 9-12): Measured governance by outcomes, not inputs
Traditional governance measurement: policies written, training completed, audits conducted. These measure governance theater, not governance effectiveness.
They measured governance outcomes: AI incidents per million interactions (target: less than 10 critical incidents per million). Bias detection rate (percentage of biased outputs caught before reaching users, target greater than 95%). Hallucination containment speed (time from detection to remediation, target less than 4 hours). Accountability resolution rate (percentage of incidents where root cause and responsibility determined, target greater than 90%).
Monthly governance reviews assessed: Are incidents increasing or decreasing? Are automated controls catching more problems or are users encountering failures? Is incident response getting faster or slower? Are users developing healthy skepticism toward AI outputs or over-relying without verification?
Adjusted governance based on outcomes: If incidents increase, strengthen automated controls before expanding deployment. If false positive rate too high, tune detection to reduce noise. If containment speed slows, add automation to response procedures.
Result: After 12 months at 80,000 users, critical incident rate: 7 per million interactions (below target). Bias detection: 97% caught before reaching users. Hallucination containment: average 2.3 hours from detection to remediation. User surveys show healthy AI skepticism: 89% of engineers report they verify AI suggestions before accepting, rather than trusting blindly. Governance effectiveness improved while deployment scale increased 160x (from 500 to 80,000 users).
Do This Next
Week 1-2: Assess your AI deployment scale and governance maturity
Document current AI deployment: How many users? How many interactions daily? What data does AI access? What decisions or outputs does AI influence? What’s your projected growth over next 12-24 months?
Evaluate governance maturity: Was governance designed for pilot (hundreds of users) or production scale (thousands to millions)? Do you have automated monitoring or only manual audits? Can you detect and respond to AI failures within hours, or does it take days/weeks? Can you measure governance effectiveness (incident rates, detection accuracy) or only governance activity (policies written, training completed)?
Red flags indicating governance won’t scale: All AI output review is manual (human review of AI suggestions, manual audits of AI interactions). No automated anomaly detection (relying on users to report problems). Incident response designed for individual remediation (addressing issues one user at a time). Governance measured by inputs not outcomes (counting policies, not measuring incident rates).
Week 3-4: Build automated governance monitoring
Implement automated controls that scale with deployment: Real-time scanning of AI outputs for high-risk patterns (security vulnerabilities, bias indicators, hallucination markers, compliance violations). Anomaly detection flagging unusual behavior (AI generating outputs inconsistent with expected use cases, sudden changes in output patterns, clusters of similar problematic outputs). Automated blocking of critical failures before outputs reach users (credentials in code suggestions, vulnerable code patterns, outputs violating security policies).
Start with highest-risk use cases: If AI accesses sensitive data, implement automated data access monitoring. If AI generates customer-facing content, implement bias and appropriateness scanning. If AI influences financial decisions, implement hallucination and accuracy checking.
Week 5-8: Implement tiered access controls
Not all users need same level of AI capability or governance oversight. Tier access based on risk: Low-risk users (junior staff, general productivity tasks, non-sensitive data): AI with standard monitoring. Medium-risk users (experienced staff, important but not critical decisions, moderately sensitive data): AI with enhanced monitoring and periodic human review. High-risk users (executives, critical systems, highly sensitive data): AI with real-time human oversight, restricted capabilities, comprehensive logging.
Build controls that scale: Don’t rely on manual classification of every user. Create role-based access control (RBAC) that automatically assigns AI access tier based on job role, data access permissions, and system criticality. Adjust automatically as roles change.
Week 9-12: Build incident response that scales to mass deployment
Prepare for incidents affecting thousands or millions: Automated detection systems that identify AI failures at scale (pattern recognition across millions of interactions, not individual output review). Mass notification capability that can reach all users within minutes (not hours or days). Automated rollback procedures (disable problematic AI model, roll back to previous version, notify users, investigate). Runbooks for different incident types (hallucination, bias, security failure, compliance violation).
Test incident response via tabletop: “AI model generates systematically biased outputs affecting 10,000 users over 24 hours before detection. Walk me through first 4 hours of response.” Identify gaps: Who makes decision to disable model? How do you notify 10,000 users? How do you identify which outputs were problematic? What’s rollback procedure? Improve response procedures based on identified gaps.
Week 13-16: Measure governance outcomes, not theater
Shift metrics from governance activity to governance effectiveness: Stop measuring: Policies written, training completed, audits conducted. Start measuring: AI incidents per million interactions (how often does AI fail in problematic ways?), bias detection rate (what percentage of biased outputs caught before reaching users?), hallucination containment speed (time from detection to remediation), accountability resolution rate (percentage of incidents where root cause determined), user verification behavior (do users verify AI outputs or trust blindly?).
Review monthly: Are incidents increasing or decreasing? Are detection systems effective? Is response getting faster? Are users developing healthy AI skepticism?
Adjust governance based on outcomes: If incidents increase, strengthen controls before further deployment. If false positives too high, tune detection. If containment slow, add automation. If users over-rely on AI, strengthen training on verification.
Decision tree: If deploying AI to fewer than 1,000 users with limited scope, manual governance may be sufficient. If deploying to 1,000-10,000 users OR high-risk use cases, implement automated monitoring and tiered access. If deploying to greater than 10,000 users OR mission-critical use cases, full automated governance required (real-time monitoring, automated blocking, scalable incident response, outcome-based measurement).
Script for executive approval: “We’re scaling AI deployment from X thousand to Y hundred thousand users over next 12 months. Our current governance was designed for pilot scale—manual review, individual accountability, limited monitoring. This won’t scale. I need approval to build automated governance: real-time monitoring of AI outputs, automated blocking of high-risk patterns, tiered access controls based on risk, and incident response that can reach all users within minutes. The alternative is deploying AI at scale without governance capable of catching failures before they harm users, customers, or operations. Recent incidents across industry show what happens when governance doesn’t scale with deployment—we can avoid that by investing in scalable controls now.”
One Key Risk
You implement aggressive automated governance monitoring and blocking. AI suggestions get blocked frequently. Users complain AI is “less useful” because automated controls restrict capabilities. Productivity gains that justified AI investment don’t materialize because governance made AI too restrictive. Leadership pressures you to relax controls to restore AI utility, creating tension between security/compliance and productivity.
Mitigation: Tier governance controls by risk, not blanket restrictions. Low-risk use cases (general productivity, non-sensitive data, non-critical decisions) get minimal restrictions—maximize utility, accept higher risk. High-risk use cases (sensitive data, critical systems, high-impact decisions) get strong controls—accept reduced utility in exchange for risk mitigation. Communicate trade-offs clearly: “We’re restricting AI access to customer PII because governance at scale requires limiting exposure. You can still use AI for general tasks, just not with sensitive data.” Measure impact: Track productivity metrics before/after governance implementation. If governance reduces productivity significantly, identify specific pain points and adjust controls to minimize friction while maintaining protection. Build feedback loops: When users complain about blocked suggestions, investigate—was it legitimate block (preventing actual risk) or false positive (preventing safe activity)? Adjust detection logic to reduce false positives while maintaining security.
Bottom Line
AI adoption is scaling to hundreds of millions of users faster than governance frameworks can adapt. India’s 100 million weekly active OpenAI users signals that AI has crossed the threshold from enterprise pilot to population-scale infrastructure. Governance frameworks designed for thousands of pilot users—manual output review, individual accountability, human-paced incident response—fail at millions of production users. Organizations that build automated governance monitoring, implement tiered access controls based on risk, prepare incident response for mass deployment, and measure governance by outcomes (incident rates, detection accuracy, containment speed) will scale AI safely. Organizations that scale deployment without scaling governance will discover that controls designed for pilots don’t prevent failures at production scale—and the failures will affect thousands or millions of users before governance catches them.
Story 2 — Compliance Deadlines Don’t Guarantee Remediation
What Happened
CISA added three vulnerabilities to its Known Exploited Vulnerabilities (KEV) catalog on February 10, 2026, with mandatory remediation deadline of February 16, 2026 for all federal agencies and recommended remediation for critical infrastructure and enterprises:
CVE-2026-1731: BeyondTrust Privileged Remote Access and Remote Support – Path traversal vulnerability allowing attackers to gain unauthorized access to privileged remote access systems. Actively exploited in attacks targeting federal agencies. CVSS score: 9.8 (Critical).
SolarWinds Web Help Desk: Remote code execution vulnerability allowing attackers to execute arbitrary code on vulnerable systems. Already exploited in campaigns targeting IT service management infrastructure. CVSS score: 9.1 (Critical).
Microsoft Office Word: Memory corruption vulnerability allowing attackers to execute malicious code when victims open specially crafted Word documents. Exploited in spearphishing campaigns delivering ransomware and information stealers. CVSS score: 7.8 (High).
February 16, 2026 is the compliance deadline. Federal agencies must either remediate these vulnerabilities (deploy patches, implement workarounds, or remove vulnerable systems from production) or document exceptions explaining why remediation cannot be completed and what compensating controls are in place.
The KEV catalog exists because known vulnerabilities remain unpatched for extended periods despite active exploitation. CISA analysis shows that adversaries routinely exploit publicly disclosed vulnerabilities within days or weeks of disclosure, yet many organizations take months to deploy patches. The KEV catalog with mandatory remediation deadlines aims to accelerate patching by creating compliance pressure—but compliance pressure doesn’t guarantee actual remediation.
Why It Matters
Compliance deadlines measure policy adherence, not security posture. Organizations can “meet the deadline” through multiple pathways:
Actual remediation: Deploy patches, validate fixes, confirm vulnerabilities are resolved. This improves security posture.
Exception documentation: Document why systems can’t be patched immediately (business criticality, patch testing delays, vendor patch unavailability), identify compensating controls (network segmentation, access restrictions, monitoring), file exception with approval from senior leadership. This meets compliance requirement but leaves systems vulnerable.
Scheduled maintenance: Document plan to patch during next maintenance window (which might be weeks or months in future), show that timeline meets “reasonable” remediation pace, demonstrate due diligence in planning. This meets compliance documentation requirement but delays actual risk reduction.
Risk acceptance: Document decision to accept vulnerability rather than remediate (cost of patching exceeds assessed risk, system scheduled for decommission, alternative solutions planned), obtain executive approval. This meets compliance requirement but explicitly chooses to remain vulnerable.
The result: Organizations can achieve “compliant” status while actual vulnerability remediation is incomplete. Compliance frameworks reward documentation and process adherence, not necessarily risk reduction.
The disconnect matters because attackers don’t care about compliance status—they care about exploitable vulnerabilities. If organization meets February 16 deadline by documenting exceptions but doesn’t actually patch vulnerable systems, those systems remain exploitable regardless of compliance documentation. Attackers scan for vulnerable BeyondTrust, SolarWinds, and Microsoft Office systems without checking whether victims have filed exception paperwork.
The fundamental problem: Compliance frameworks operate on human timelines (days to weeks for approval, documentation, scheduled maintenance). Attackers operate on machine timelines (hours to days from vulnerability disclosure to active exploitation). The gap between compliance deadline (February 16) and actual patching (potentially weeks or months later for systems covered by exceptions) is where breaches happen.
Operational Exposure
If your organization’s vulnerability management strategy is driven by compliance deadlines rather than exploitation timelines, you’re measuring success by policy adherence rather than risk reduction.
This affects:
Federal agencies: Must meet CISA KEV deadlines or face compliance violations, potential budget impacts, Congressional oversight questions. But meeting deadlines through exceptions doesn’t prevent breaches—just avoids compliance penalties.
Critical infrastructure: CISA KEV catalog is “recommended” not mandatory, but cyber insurance and regulatory oversight increasingly expect adherence. Organizations face pressure to document compliance even if actual remediation delayed.
Enterprises: No direct regulatory requirement to meet CISA KEV deadlines, but cyber insurance, customer security requirements, and industry best practices create pressure to demonstrate “good cyber hygiene” including timely patching. Meeting deadlines through documentation satisfies auditors but doesn’t prevent exploitation.
Security teams: Face competing pressures—leadership demands compliance (meet deadlines, file exceptions, demonstrate due diligence) vs. actual security (patch vulnerable systems before exploitation). When these pressures conflict, compliance often wins because it’s measurable, auditable, and avoids regulatory penalties—even though it doesn’t prevent breaches.
Who’s Winning
One federal contractor managing critical infrastructure security systems implemented operational vulnerability management in Q3 2025 after observing that compliance-driven patching left them exposed despite “meeting deadlines.” Their approach:
Phase 1 (Ongoing): Treat CISA KEV additions as operational triggers, not compliance deadlines
Traditional approach: CISA adds vulnerability to KEV catalog with deadline. Organization validates if they’re affected, assesses priority based on compliance deadline, plans patching around normal change management cycles, meets deadline through combination of actual patches and documented exceptions. Result: Compliant on deadline day, but vulnerable systems remain exposed for weeks or months.
Their operational approach: CISA adds vulnerability to KEV catalog. Immediately (within 4 hours) validate if they’re exposed: Do we run affected software? Is it internet-accessible? Is it in production? Assess business impact if exploited: What breaks if this system is compromised? What data exposed? What services disrupted? Deploy patches to critical systems within 48-72 hours regardless of compliance deadline or normal change management cycles. Document compliance after remediation, not instead of remediation.
They implemented tiered response based on actual risk, not compliance deadlines: Tier 1 (Critical exposure): Affected systems are internet-accessible AND process sensitive data OR control critical infrastructure. Patch within 48 hours. Accept risk of rushed patching (test in non-production first, but don’t delay production patching for extensive testing). Tier 2 (High exposure): Affected systems are internal-facing OR less critical functions. Patch within 1 week. Standard change management applies. Tier 3 (Limited exposure): Affected systems are isolated OR scheduled for decommission. Patch during next maintenance window OR document plan to decommission. Accept residual risk during transition.
Result: When CVE-2026-1731 (BeyondTrust) added to KEV catalog February 10 with February 16 deadline, they validated exposure within 3 hours (6 BeyondTrust systems in production, 4 internet-accessible), assessed impact (systems provide privileged remote access for IT administrators—compromise equals domain admin access), and deployed patches to 4 internet-accessible systems within 36 hours (February 11). Remaining 2 internal systems patched within 5 days. Documented compliance after patching complete. Contrast with compliance-driven approach: organizations that prioritized documentation over patching met February 16 deadline through exceptions but remained vulnerable to CVE-2026-1731 exploitation for weeks afterward.
Phase 2 (Monthly): Measure actual remediation rate, not compliance documentation rate
Traditional metrics: Percentage of systems meeting compliance deadlines (measures documentation), percentage of exceptions filed and approved (measures process), average time from deadline to documented compliance (measures bureaucracy). These measure compliance theater, not vulnerability reduction.
Their operational metrics: Percentage of critical vulnerabilities remediated within 48 hours of CISA KEV addition (measures speed of actual patching, not compliance documentation). Percentage of vulnerable systems actually patched vs. covered by exceptions (distinguishes real remediation from documented acceptance). Time from vulnerability disclosure to exploitation in the wild vs. time to patch deployment (measures whether they’re patching faster than attackers are exploiting). Number of vulnerable systems remaining exposed 30 days after CISA KEV addition (measures residual risk after compliance deadline passes).
Monthly reviews assessed: Are we patching faster or slower than last month? Are exceptions increasing or decreasing? Are we patching before or after exploitation observed in wild? Do our exceptions actually reduce exposure or just document vulnerability?
Adjusted approach based on results: If exceptions increasing, identify why (patching process broken? Change management too slow? Lack of resources?) and fix root causes. If time to patch increasing, identify bottlenecks and eliminate friction. If vulnerabilities remaining exposed 30+ days after deadline, escalate to executive leadership as unacceptable risk.
Result: After 12 months, critical vulnerability remediation within 48 hours: 87% (up from 34% before operational approach). Vulnerable systems actually patched vs. exceptions: 92% actual patches, 8% documented exceptions (versus 61% actual patches, 39% exceptions under compliance-driven approach). Time to patch vs. exploitation timeline: average 2.8 days from CISA KEV addition to patch deployment, versus average 14 days from disclosure to observed exploitation in wild—patching faster than attackers. Vulnerable systems remaining exposed 30+ days after deadline: 3% (versus 31% under compliance approach).
Phase 3 (Quarterly): Test whether patches actually deployed and effective
Compliance documentation says patches deployed. Operational reality: patches fail to deploy due to system incompatibilities, patches deploy but don’t actually fix vulnerability due to configuration issues, systems get re-imaged or restored from old backups that lack patches.
They implemented quarterly validation: Vulnerability scanning of all systems covered by CISA KEV remediation in past 90 days. Validate: Are patches actually present? Are vulnerabilities actually resolved? Are there systems we thought were patched but aren’t?
When validation identifies gaps: Investigate root cause (why did patch fail?), remediate immediately (not wait for next maintenance window), document failure and preventive measures.
Result: Quarterly validation discovered 12% of systems believed to be patched still showed vulnerable in scans. Root causes: 5% were backup/DR systems restored from old images that lacked patches, 4% were patches that deployed but didn’t apply due to configuration conflicts, 3% were systems where patch “deployment succeeded” per logs but software restart required to activate patch never occurred. All discovered gaps remediated within 48 hours of detection. Process improvements implemented to prevent recurrence (automated patching of backup images, validation that patches actually fixed vulnerabilities not just deployed, automated system restart after critical patches).
Do This Next
Week 1: Change vulnerability management from compliance-driven to risk-driven
Stop: Prioritizing vulnerabilities based on compliance deadlines, meeting deadlines through documentation instead of patching, measuring success by percentage of deadlines met.
Start: Prioritizing vulnerabilities based on exploitation timelines (are attackers actively exploiting? how fast?), actual risk (what breaks if exploited? what data exposed?), and remediation urgency (patch critical exposures within 48-72 hours regardless of compliance deadline).
Assessment: Pull last 12 months of CISA KEV vulnerabilities. For each: When did you actually patch vs. when was compliance deadline? How many you patched within 48 hours? Within 1 week? How many met deadline through exceptions? How long did systems covered by exceptions remain vulnerable after deadline?
Red flags: More than 20% of KEV vulnerabilities met through exceptions rather than actual patches. Average time to patch exceeds 7 days. Systems remain vulnerable 30+ days after compliance deadline.
Week 2-3: Implement operational response to CISA KEV additions
When CISA adds vulnerability to KEV catalog: Within 4 hours: Validate exposure (do we run affected software? where? is it accessible to attackers?). Within 8 hours: Assess business impact (what breaks if exploited? what data exposed? what’s criticality?). Within 24 hours: Classify by tier (Tier 1: internet-accessible + critical function = patch within 48 hours. Tier 2: internal + important function = patch within 7 days. Tier 3: isolated + low criticality = patch next maintenance window). Deploy patches based on tier urgency, not compliance deadline. Document compliance after remediation, not instead of it.
Build fast-path change management for critical vulnerabilities: Tier 1 vulnerabilities bypass normal change approval (pre-approved by CISO, executed by security team with operations notification). Testing: Deploy to non-production first, validate it doesn’t break functionality, but don’t delay production patching for extensive testing—test basics, deploy to production, monitor closely, roll back if breaks. Accept calculated risk: Some Tier 1 patches might cause operational disruption. This is preferable to leaving critical vulnerabilities exploitable for weeks while conducting extensive testing.
Week 4: Measure remediation effectiveness, not compliance theater
Implement operational metrics: Time from CISA KEV addition to patch deployment (median and 90th percentile for Tier 1, Tier 2, Tier 3). Percentage of vulnerabilities remediated via actual patches vs. exceptions. Number of systems remaining vulnerable 30/60/90 days after compliance deadline. Patch deployment success rate (patches attempted vs. patches successfully deployed and validated).
Monthly review: Are we patching faster this month than last month? Are exceptions increasing (bad trend) or decreasing (good trend)? Are we patching before exploitation widely observed or after? When patches fail to deploy, what’s root cause and how do we prevent recurrence?
Escalate to leadership: If greater than 20% of critical vulnerabilities meet deadline through exceptions, escalate as “unacceptable risk exposure.” If average time to patch exceeds exploitation timeline (faster than 7 days in most cases), escalate as “we’re losing race against attackers.” If systems remain vulnerable 30+ days after deadline, escalate as “compliance documentation doesn’t prevent breaches.”
Week 5-8: Build patch validation and verification
Implement quarterly vulnerability scanning: All systems covered by CISA KEV remediation in past 90 days. Validate: Are patches actually present? Do scans confirm vulnerabilities resolved? Are there systems believed patched but still vulnerable?
When validation finds gaps: Investigate immediately (why did patch fail? was it never deployed? did it deploy but not apply? did system get restored from unpatched backup?). Remediate within 48 hours (don’t wait for next maintenance cycle—vulnerable system is vulnerable system). Document root cause and preventive measures (how do we prevent this failure mode from recurring?).
Fix systemic issues: If backup systems restored without patches, implement automated patching of backup images. If patches deploy but don’t apply due to configuration conflicts, improve pre-deployment testing. If patches deploy but require system restart that never happens, automate restart procedures.
Decision tree: If you’re federal agency or critical infrastructure, operational vulnerability management is mandatory—compliance deadlines aren’t optional but treating them as operational triggers improves security beyond mere compliance. If you’re enterprise without regulatory requirement, operational approach still recommended—cyber insurance and customer security requirements increasingly expect demonstrated patching velocity. If you’re small organization with limited security staff, focus operational response on Tier 1 critical exposures—patch internet-accessible systems with critical vulnerabilities within 48-72 hours, let less critical systems follow normal change management.
Script for executive leadership: “CISA added three vulnerabilities to KEV catalog with February 16 deadline. We have X systems affected. Current process: we’ll validate exposure, file change requests, schedule maintenance windows, meet deadline through combination of patches and documented exceptions. New approach: we’ll validate exposure within 4 hours, classify by actual risk, patch Tier 1 critical systems within 48 hours regardless of normal change management, and document compliance after we’ve actually reduced risk. This means some changes bypass normal approval processes—but attackers are exploiting these vulnerabilities within days of disclosure. Meeting compliance deadlines through documentation doesn’t prevent breaches. Patching vulnerable systems before exploitation does. I need approval to implement fast-path change management for critical vulnerabilities so we can patch before attackers exploit, not after.”
One Key Risk
You implement aggressive operational patching—Tier 1 vulnerabilities patched within 48 hours, bypassing normal change management. A critical patch breaks production system, causes service outage, impacts business operations. Leadership questions why “we rushed a patch without proper testing,” loses trust in fast-path change management, reverts to compliance-driven approach where all patches require extensive testing and approval—which restores the weeks-long delays that leave systems vulnerable.
Mitigation: Build trust through successful fast-path deployments before critical incident. Start with lower-risk Tier 1 systems (development, staging, non-customer-facing) to demonstrate fast-path works without breaking things. Test in non-production first—but keep testing minimal (does system boot? do basic functions work?) rather than extensive (test every edge case). Monitor closely after production deployment—if problems emerge, catch them within minutes/hours, roll back quickly. Communicate risk trade-offs clearly: “This vulnerability is actively exploited. We can test extensively and patch in 2 weeks, leaving us vulnerable during that time. Or we can test basics and patch in 48 hours, accepting small risk of disruption in exchange for closing exploitation window. Given active exploitation, we recommend fast-path.” If patch does break something, conduct blameless postmortem, identify what would have caught issue in testing, adjust process—but don’t abandon fast-path entirely. One failure doesn’t invalidate approach when alternative (weeks-long vulnerability exposure) has higher risk.
Bottom Line
Compliance deadlines measure policy adherence, not security posture. Organizations can “meet CISA KEV deadlines” through documentation and exceptions while vulnerable systems remain exploitable for weeks or months after the deadline passes. Attackers don’t check compliance status before exploiting vulnerabilities—they scan for vulnerable systems and exploit them regardless of whether victims filed proper exception paperwork. Organizations that treat CISA KEV additions as operational triggers (validate exposure within hours, patch critical systems within 48-72 hours based on actual risk rather than compliance deadline, measure by actual remediation rate rather than documentation rate) will reduce exploitation exposure. Organizations that meet deadlines through compliance theater will discover that documentation doesn’t prevent breaches—actual patching does.
Source: https://www.cisa.gov/known-exploited-vulnerabilities-catalog
Story 3 — Nation-State Actors Weaponize Enterprise AI Tools
What Happened
Google’s Threat Intelligence Group released analysis on February 14, 2026 documenting how North Korea’s state-backed hacking collective UNC2970 is using Google Gemini—Google’s enterprise AI assistant—to conduct reconnaissance operations against targets in the cybersecurity and defense sectors.
UNC2970’s Gemini usage patterns observed by Google threat researchers: Synthesizing open-source intelligence (OSINT) from multiple public sources (LinkedIn, company websites, job postings, technical blogs, conference presentations) into comprehensive target profiles. Profiling high-value individuals at target organizations including technical job roles and responsibilities, reporting relationships and organizational structure, salary ranges and compensation expectations, technical skills and project experience, social media activity and personal interests. Mapping organizational technical infrastructure based on publicly available information including job postings mentioning specific technologies, technical documentation and white papers published by organization, conference presentations describing architecture choices, GitHub repositories and open-source contributions, vendor relationships and technology partnerships.
Supporting campaign planning with AI-generated analysis including vulnerability research based on public disclosures, spearphishing email templates tailored to target profiles, social engineering strategies exploiting identified personal interests, and technical approaches optimized for target infrastructure.
Google’s analysis emphasizes that UNC2970 is using Gemini for legitimate research purposes—synthesizing public information, analyzing technical documentation, generating summaries—but applying those capabilities to malicious reconnaissance. The AI doesn’t “know” it’s being used for attack planning. It responds to prompts requesting research and analysis without distinguishing between legitimate business intelligence and adversarial targeting.
This isn’t isolated to UNC2970 or Gemini. Security researchers have documented similar patterns across threat groups: Chinese APT groups using ChatGPT to analyze target organizations and plan campaigns. Russian threat actors using Claude to generate spearphishing content. Criminal groups using various AI assistants to automate reconnaissance at scale. Iranian threat actors using AI to synthesize intelligence from multiple sources.
The pattern is consistent: Enterprise AI tools designed for legitimate research, analysis, and productivity are being weaponized for reconnaissance, target profiling, and attack planning by adversaries who recognize that AI dramatically accelerates intelligence gathering operations.
Why It Matters
Enterprise AI tools—ChatGPT, Claude, Gemini, Microsoft Copilot—are designed to help users synthesize information from multiple sources, analyze complex data, generate insights, and produce high-quality content efficiently. These capabilities are valuable for legitimate business purposes: researching competitors, analyzing market trends, profiling potential customers or partners, synthesizing technical documentation, and generating communications.
These same capabilities enable adversarial operations: Researching target organizations to identify vulnerabilities. Analyzing public information to map infrastructure and identify attack surfaces. Profiling individuals to craft personalized spearphishing attacks. Synthesizing intelligence from multiple sources that individually seem innocuous but collectively reveal sensitive information. Generating malicious content (phishing emails, social engineering scripts) optimized for specific targets.
The fundamental challenge: AI can’t reliably distinguish between “legitimate business intelligence” and “malicious reconnaissance.” When user prompts Gemini to “analyze this organization’s technical infrastructure based on public job postings and technical blogs,” AI doesn’t know whether user is legitimate security researcher, competitive intelligence analyst, potential partner conducting due diligence, or adversary planning attack. The prompt is identical across use cases.
Traditional reconnaissance required adversaries to manually research targets—reading websites, analyzing job postings, reviewing LinkedIn profiles, synthesizing information across sources. This was time-consuming, resource-intensive, and scalable only with significant human effort. Adversaries could target limited number of organizations at depth because manual reconnaissance didn’t scale.
AI removes those constraints. UNC2970 can prompt Gemini to analyze hundreds of target organizations in the time manual research would cover ten. They can generate comprehensive profiles of key employees across multiple organizations. They can synthesize technical intelligence from diverse sources that would take human analysts weeks to compile. Reconnaissance that required teams of analysts for weeks can now be executed by single individual with AI in hours or days.
The scale and speed advantage compounds: Adversaries using AI for reconnaissance can target more organizations, develop deeper target understanding, identify more attack vectors, and move faster from reconnaissance to exploitation than defenders can respond. Organizations discover they’re being targeted only after exploitation begins—by which time adversary has already conducted comprehensive reconnaissance, identified optimal attack paths, and crafted personalized exploitation strategies.
Operational Exposure
If your organization assumes adversaries conduct reconnaissance manually and therefore reconnaissance takes time that defenders can use to detect and respond, you’re defending against pre-AI threat actors. Modern adversaries using enterprise AI tools for reconnaissance operate at speeds and scales that human-paced defenses can’t match.
This affects:
Cybersecurity and defense companies: UNC2970 specifically targets this sector, using AI to profile organizations that develop security tools, analyze their technical approaches, identify potential vulnerabilities in their own defenses, and plan operations against defenders. Irony: companies that build security tools are being researched via AI by the adversaries they’re defending against.
Executive and technical leadership: High-value individuals get profiled via AI synthesis of public information (LinkedIn, conference talks, social media, published articles). Adversaries build comprehensive understanding of targets’ technical expertise, responsibilities, reporting relationships, personal interests—everything needed for personalized spearphishing or social engineering.
Technical infrastructure: Public job postings, technical blogs, conference presentations, GitHub repositories all contribute to adversary understanding of your infrastructure. AI synthesizes these disparate sources into coherent technical architecture maps that would take human analysts significant time to compile manually.
Security operations: Reconnaissance is often first phase of attack lifecycle, but if reconnaissance happens via AI at machine speed using publicly available information, traditional reconnaissance detection (unusual scanning activity, suspicious queries, anomalous access patterns) won’t catch it. Adversaries never touch your systems during AI-powered reconnaissance—they’re just asking AI to analyze public data.
Who’s Winning
One cybersecurity company recognized in Q4 2025 that adversaries would use AI for reconnaissance against them—they build defensive tools, making them high-value targets for nation-state actors who want to understand their capabilities and identify vulnerabilities. Their approach:
Phase 1 (Weeks 1-4): Reduced public information adversaries could feed to AI
Traditional approach: Publish freely to demonstrate expertise, recruit talent, attract customers. Post detailed job descriptions mentioning specific technologies. Employees share technical details on LinkedIn. Engineers present architecture choices at conferences. Publish technical blogs explaining implementation approaches. Share code in public GitHub repositories.
This serves legitimate purposes but also feeds adversary reconnaissance.
They implemented OSINT minimization: Sanitized job postings to remove excessive technical detail. Changed: “Senior Engineer – Kubernetes, Istio service mesh, Cilium networking, eBPF instrumentation, Rust backend services” to “Senior Engineer – Cloud infrastructure and networking experience required. Technical details discussed with qualified candidates.” Still attracts qualified applicants, reduces infrastructure exposure to AI reconnaissance.
Reviewed employee LinkedIn profiles for security-sensitive details. Guideline: Describe skills and experience without revealing current projects, internal architecture, or specific technologies in production use. “Worked on cloud security infrastructure” rather than “Implemented eBPF-based network security monitoring for container workloads using Cilium and Hubble.”
Limited technical conference presentations to high-level approaches without implementation details. Discuss challenges solved and outcomes achieved, not specific tools and architectures that reveal internal infrastructure.
Audited public GitHub repositories for unintentional information disclosure. Found: Code comments revealing internal tool names and architectures. Configuration files pointing to internal services. Documentation describing internal workflows. Sanitized or removed before adversaries could feed to AI for analysis.
Result: Reduced public technical detail available for AI reconnaissance by estimated 70% while maintaining ability to recruit, market, and engage with technical community. Adversaries using AI still find public information but get less detailed technical intelligence.
Phase 2 (Weeks 5-8): Monitored for reconnaissance indicators
Traditional approach: Assume reconnaissance involves scanning your systems, probing your infrastructure, attempting unauthorized access. These generate logs and trigger alerts.
AI reconnaissance problem: Adversaries never touch your systems. They prompt AI with publicly available information and get intelligence without ever interacting with your infrastructure. Traditional detection doesn’t catch it.
They implemented alternative reconnaissance detection: Monitored for patterns suggesting systematic information gathering: Unusual spikes in LinkedIn profile views of multiple employees across different teams within short timeframe. Systematic enumeration of employees via LinkedIn (viewing profiles in organizational hierarchy order, viewing everyone in specific department). Suspicious questions submitted via customer support or sales inquiries that probe for technical details beyond what customers typically ask. Attempts to engage employees via social media with questions about internal technologies or projects.
Built intelligence on adversary research patterns: Honeytokens in public spaces (fake employee profiles on LinkedIn mentioning non-existent projects, job postings for non-existent positions mentioning fake technologies). If adversaries research these honeytokens (AI includes them in reconnaissance reports, spearphishing references them), indicates targeting activity.
Result: Detected three likely reconnaissance campaigns over 9 months: Pattern of systematic LinkedIn profile viewing across engineering organization. Customer inquiry asking very specific technical questions about architecture choices we don’t publicly discuss (suggests adversary has intelligence from other sources and is using customer inquiry to validate). Job applicant whose questions during interview focused heavily on security architecture and internal tools (unusual for legitimate candidate at that stage).
Phase 3 (Weeks 9-12): Trained employees to recognize and resist social engineering informed by AI reconnaissance
Traditional security training: Don’t click suspicious links, verify sender identity, report phishing. Assumes adversaries use generic attacks.
AI reconnaissance enables hyper-personalized attacks: Spearphishing email references your recent LinkedIn post, conference presentation, or shared interest. Phone call from “recruiter” knows details about your role, projects, and technical skills. LinkedIn connection request from someone who appears to know your work and colleagues. Message references technical challenges relevant to your specific responsibilities.
Traditional training: “Suspicious email from unknown sender asking you to click link = phishing.” AI-enabled training: “Email from someone who seems to know you, references your work accurately, and asks reasonable-sounding questions = potential social engineering informed by AI reconnaissance of your public information.”
They implemented updated training: Examples of AI-enabled reconnaissance and social engineering. “Adversaries can use AI to analyze your LinkedIn, conference talks, and technical blog posts to build detailed profile. They use that intelligence to craft personalized attacks that feel legitimate because they reference real details about your work. Trust your instincts—if outreach feels too perfectly tailored, verify through separate channel.”
Built verification protocols: If recruiter, journalist, researcher, or potential partner reaches out with detailed knowledge of your work, verify their identity through independent means (not contact info they provided). Search for their LinkedIn profile and verify they work where they claim. Check if your colleagues know them. Call company directly using public phone number to verify they’re real employee.
Report reconnaissance attempts: If someone asks questions that probe for internal technical details, even if questions seem innocent, report to security. “Potential partner” asking detailed questions about your infrastructure could be legitimate due diligence or adversary reconnaissance—let security assess.
Result: Employees reported 12 suspicious contacts over 9 months that might have been reconnaissance or social engineering. Security investigated each. 8 were legitimate (recruiters, journalists, researchers who conducted thorough research before reaching out). 4 were suspicious enough to flag as potential adversary activity (questions probed for sensitive details, verification attempts found inconsistencies, contact abandoned interaction when pressed for verification).
Do This Next
Week 1-2: Assume adversaries use AI to research your organization
Accept reality: Adversaries can prompt ChatGPT, Claude, or Gemini to “research [your company name]” and receive comprehensive summary of public information in minutes. They can ask AI to “profile employees at [your company] working in security” and get list of names, roles, skills from LinkedIn. They can request “analyze technical infrastructure at [your company] based on job postings and public documentation” and receive intelligence synthesis human analysts would take days to compile manually.
Assessment: What public information would adversary find useful? Job postings revealing technical stack. Employee LinkedIn profiles describing current projects. Conference presentations explaining architecture. Technical blog posts detailing implementation approaches. GitHub repositories showing code and configuration. Vendor relationships and technology partnerships visible through press releases or case studies.
Red flags: Job postings that read like internal architecture documentation. Employee social media sharing excessive detail about internal projects. Conference presentations that would allow competitor to reverse-engineer your approaches. Public code repositories that reveal security-relevant implementation details.
Week 3-4: Minimize OSINT exposure without hurting recruitment or marketing
Balance competing needs: Need to recruit (requires describing roles, technologies, interesting work). Need to market (requires demonstrating expertise, sharing success stories, engaging community). But also need to limit information adversaries can use for reconnaissance.
Sanitize job postings: Describe skills and experience required without cataloging entire technology stack. Change: “Senior Cloud Security Engineer – Experience with AWS, Kubernetes, Istio, Cilium, eBPF, Falco, Terraform, Golang required. Will work on implementing zero-trust networking using service mesh and network policy enforcement via eBPF instrumentation.” To: “Senior Cloud Security Engineer – Cloud infrastructure and container security experience required. Strong background in networking and security automation. Technical details discussed with qualified candidates.”
Guide employee social media: Suggest employees describe skills and past experience without revealing current projects, technologies in production, or internal architecture. “Worked on cloud security infrastructure achieving X% improvement in detection capability” rather than “Built custom eBPF instrumentation using Cilium and Hubble to monitor container network traffic, deployed to 10,000-node Kubernetes clusters running Istio service mesh.”
Review conference presentations and technical blogs: Share insights and approaches without implementation details that reveal internal infrastructure. Discuss challenges, solutions, and outcomes. Omit specific tools, architectures, and configurations that allow adversaries to map your technical environment.
Audit public repositories: Review code comments, configuration files, documentation for unintentional information disclosure. Remove or sanitize references to internal services, tools, architectures before adversaries discover and feed to AI.
Week 5-8: Monitor for reconnaissance indicators
Assume AI reconnaissance doesn’t generate traditional detection signals: Adversaries never scan your systems, probe your infrastructure, or attempt unauthorized access during reconnaissance phase. They just prompt AI with public information. Traditional detection (anomalous traffic, unauthorized access attempts, vulnerability scanning) doesn’t catch AI reconnaissance.
Implement alternative detection: Monitor for systematic information gathering patterns: Unusual spike in LinkedIn profile views across multiple employees within short timeframe. Systematic viewing of profiles in organizational hierarchy order (viewing entire department one by one). Suspicious questions via customer support or sales that probe for internal technical details. Attempts to engage employees via social media with questions about projects or technologies.
Deploy honeytokens: Create fake employee LinkedIn profiles mentioning non-existent projects. Post job openings for non-existent positions mentioning fake technologies. If adversaries research honeytokens (reference them in spearphishing, probe for more details, attempt to target fake employees), indicates reconnaissance activity targeting your organization.
Build intelligence on reconnaissance patterns: When detect potential targeting, document: Who was targeted (which employees, which departments)? What information was sought (technical details? organizational relationships? personal information?)? What methods were used (LinkedIn, social media, customer inquiries, job applications?)? Share patterns with security team to improve detection.
Week 9-12: Train employees to recognize AI-enabled social engineering
Update security training for AI reconnaissance reality: Traditional phishing training: “Suspicious email from unknown sender asking you to click link.” AI-enabled social engineering: “Email from someone who seems to know you, accurately references your work, and asks reasonable-sounding question—but something feels slightly off.”
Provide examples: Recruiter email that references your recent LinkedIn post, conference talk, or technical blog and offers role perfectly matched to your skills. Journalist inquiry that demonstrates detailed knowledge of your work and asks for interview about specific project. Potential partner or vendor who knows details about your role and asks questions about technologies you use. All could be legitimate—or adversary using AI reconnaissance to craft personalized approach.
Teach verification: If outreach feels too perfectly tailored, verify through independent channel. Search for sender’s LinkedIn profile and verify they work where they claim. Check if colleagues know them. Call company directly using public phone number, not contact info provided in message. Ask security team to investigate if suspicious.
Encourage reporting: If anyone asks questions that probe for internal technical details beyond what public role would need, report to security even if interaction seems innocent. “Potential partner” doing due diligence could be legitimate or adversary reconnaissance. Let security assess.
Decision tree: If you’re in cybersecurity, defense, critical infrastructure, or other high-value target sector, assume nation-state adversaries use AI for reconnaissance against you—implement OSINT minimization and reconnaissance detection. If you’re enterprise in competitive sector, assume competitors may use AI for competitive intelligence (legal but still reconnaissance)—balance public transparency for legitimate purposes against information exposure. If you’re organization with limited threat profile, focus on sanitizing job postings and guiding employee social media—most cost-effective OSINT minimization without major changes to operations.
Script for employee communication: “Adversaries including nation-state groups are using AI to research organizations and employees. They can analyze your LinkedIn, conference talks, and technical posts in minutes to build detailed profiles. They use that intelligence to craft personalized attacks that feel legitimate because they reference your actual work. We’re asking everyone to: Review your LinkedIn profile and remove excessive detail about current projects and internal technologies. When contacted by recruiters, journalists, or potential partners who seem to know your work unusually well, verify their identity through independent means before sharing additional information. Report to security if anyone asks questions probing for internal technical details. This isn’t about hiding your expertise—it’s about not making adversaries’ reconnaissance easy.”
One Key Risk
You implement OSINT minimization—sanitize job postings, ask employees to limit social media detail, restrict conference presentations. Recruiting becomes harder because job descriptions are less specific. Marketing team complains they can’t demonstrate expertise effectively. Employees frustrated that they can’t share their work. Leadership questions why “we’re hiding instead of showcasing our capabilities.”
Mitigation: Tier OSINT minimization by sensitivity. Low-risk information (company exists, general product descriptions, leadership team) stays public—no hiding basic facts. Medium-risk information (general technical approaches, high-level architecture patterns) shared selectively—conference presentations to vetted audiences, technical blogs without implementation details. High-risk information (specific technologies in production, internal tool names, security architecture details) not shared publicly—discussed with customers under NDA, shared with qualified job candidates after initial screening. Communicate purpose clearly: “We’re not hiding—we’re making adversary reconnaissance harder. You can still describe your expertise and interesting work without cataloging our internal infrastructure.” Measure impact: Track recruitment metrics before/after OSINT minimization. If quality candidate pipeline declines significantly, identify specific changes that hurt recruitment most and adjust. Most organizations discover they can reduce OSINT exposure 50-70% without material impact on legitimate recruitment and marketing activities.
Bottom Line
Enterprise AI tools designed for legitimate research, analysis, and productivity are being weaponized by nation-state actors and criminal groups for reconnaissance at scales and speeds human defenders can’t match. North Korea’s UNC2970 using Google Gemini to profile targets demonstrates the pattern: adversaries prompt AI to synthesize public information, analyze technical infrastructure, profile employees, and support campaign planning—all without touching target systems or generating traditional reconnaissance detection signals. Organizations that assume reconnaissance takes time and generates detectable activity are defending against pre-AI threat actors. Modern reconnaissance happens at AI speed using publicly available information. Organizations that minimize OSINT exposure through sanitized job postings and guided employee social media, monitor for systematic information gathering patterns, deploy honeytokens to detect targeting, and train employees to recognize and resist AI-enabled social engineering will make adversary reconnaissance harder and more detectable. Organizations that continue publishing detailed technical information assuming only legitimate audiences will use it will discover that adversaries weaponize that information faster than defenders anticipated.
Source: https://blog.google/threat-analysis-group/
The Decision You Own
Pick one gap to close in the next 30 days:
(A) Scalable AI governance for mass adoption — Build automated monitoring of AI outputs, implement tiered access controls based on data sensitivity and risk, prepare incident response procedures that can reach thousands or millions of users within minutes, and measure governance by outcomes (incident rates, detection accuracy, containment speed) rather than inputs (policies written, training completed, audits conducted). If deploying AI to more than 10,000 users, automated governance is mandatory—manual controls don’t scale.
(B) Operational vulnerability management that beats exploitation timelines — Treat CISA KEV additions as operational triggers requiring validation within 4 hours and patching of critical exposures within 48-72 hours regardless of compliance deadlines. Implement fast-path change management for Tier 1 vulnerabilities, measure actual remediation rates rather than compliance documentation rates, and validate quarterly that patches actually deployed and resolved vulnerabilities. Compliance deadlines don’t prevent breaches—patching vulnerable systems before exploitation does.
(C) OSINT minimization and reconnaissance detection assuming AI-assisted targeting — Sanitize job postings to remove excessive technical infrastructure detail, guide employees to describe expertise without revealing current projects and internal architectures, review conference presentations and technical blogs for implementation details that reveal security-relevant information, monitor for systematic information gathering patterns indicating reconnaissance activity, and train employees to recognize and resist social engineering informed by AI reconnaissance of their public information.
AI is scaling to hundreds of millions of users faster than governance frameworks designed for thousands. Known vulnerabilities remain exploitable for weeks or months after compliance deadlines pass because meeting deadlines through documentation doesn’t reduce risk. And adversaries are using enterprise AI tools to conduct reconnaissance at speeds and scales that human-paced defenses can’t match. Organizations that build governance that scales, patch based on exploitation timelines rather than compliance deadlines, and assume adversaries use AI to research them will survive the industrialization of AI adoption, vulnerability exploitation, and reconnaissance operations. Organizations that scale deployment without scaling governance, meet compliance deadlines through documentation rather than actual patching, and assume reconnaissance happens manually will discover their controls don’t match current threat reality.
What’s Actually Changing
AI adoption is measured in hundreds of millions of users, not thousands. India’s 100 million weekly active OpenAI users signals that AI has crossed from enterprise pilot to population-scale infrastructure. Governance frameworks designed for pilot programs with thousands of users don’t scale to millions. Organizations must build automated governance monitoring, tiered access controls, and incident response that operates at machine speed—because human-paced governance can’t keep up with AI-scale deployment.
Compliance deadlines are being met through documentation rather than actual risk reduction. Organizations validate vulnerabilities, file exceptions, document compensating controls, and meet CISA KEV deadlines while vulnerable systems remain exploitable for weeks or months after the compliance deadline passes. Attackers don’t check compliance status before exploiting vulnerabilities. Organizations must treat compliance deadlines as operational triggers requiring immediate validation and rapid patching of critical exposures—measuring success by actual remediation rates, not documentation rates.
Adversaries are weaponizing enterprise AI tools for reconnaissance at scales and speeds human defenders can’t match. North Korea’s UNC2970 using Google Gemini to profile targets, synthesize OSINT, and support campaign planning demonstrates that reconnaissance no longer requires manual research taking weeks. It happens via AI prompts in hours. Organizations must assume adversaries use AI to research them, minimize public information exposure that feeds reconnaissance, monitor for systematic targeting patterns, and train employees that personalized social engineering is now trivially easy for adversaries with AI.
The gap between “we think we’re secure” and “we’re actually secure” is widening as AI accelerates adoption, exploitation, and reconnaissance faster than human-paced defenses can adapt. Organizations that recognize this gap and build governance, patching, and defense that operates at machine speed will survive. Organizations that continue operating at human speed will discover their controls don’t work when adversaries move at AI speed.
BALANCE THE TRIANGLE LABS 