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Severity
HIGH
CVSS
7.5
Priority
0.789
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Executive
Executive Summary
Frontier AI is compressing the time between vulnerability disclosure and active exploitation to near-real-time, fundamentally breaking the assumption that defenders have a meaningful patch window. According to CrowdStrike's 2026 Global Threat Report, AI-enabled adversary attacks increased 89% year-over-year, with fastest-observed lateral movement breakout time of 27 seconds - figures that render periodic scanning cycles and CVSS-driven prioritization structurally inadequate. For CISOs and boards, this is not a technology refresh question; it is an operating model question: security programs built on human-paced detection and response timelines are misaligned with the speed at which AI-augmented adversaries now operate.
Impact Assessment
CISA KEV Status
Not listed
Threat Severity
HIGH
High severity — prioritize for investigation
Actor Attribution
HIGH
AI-enabled adversaries (generic category, CrowdStrike 2026 GTR)
TTP Sophistication
HIGH
9 MITRE ATT&CK techniques identified
Detection Difficulty
HIGH
Multiple evasion techniques observed
Target Scope
INFO
General, organizations relying on periodic vulnerability scanning and CVSS-driven prioritization; CrowdStrike Falcon Platform referenced as detection/response context
Are You Exposed?
⚠
Your industry is targeted by AI-enabled adversaries (generic category, CrowdStrike 2026 GTR) → Heightened risk
⚠
You use products/services from General → Assess exposure
⚠
9 attack techniques identified — review your detection coverage for these TTPs
✓
Your EDR/XDR detects the listed IOCs and TTPs → Reduced risk
✓
You have incident response procedures for this threat type → Prepared
Business Context
The compression of exploit windows to near-real-time means that organizations relying on quarterly or monthly vulnerability remediation cycles are structurally exposed for periods that now exceed the time adversaries need to move from initial access to lateral spread. For business leaders, this translates to higher breach probability, compressed incident response timelines that increase recovery costs, and elevated regulatory exposure in sectors where breach notification requirements apply. The 89% increase in AI-enabled adversary attacks documented by CrowdStrike signals a threat landscape shift significant enough to require a strategic review of security operating model assumptions, not just a technology investment.
You Are Affected If
Your organization operates internet-facing systems and relies on periodic (weekly or monthly) vulnerability scanning rather than continuous monitoring
Your vulnerability prioritization process is driven primarily by CVSS scores without runtime exposure or exploitation likelihood weighting
Your environment includes over-privileged service accounts or incomplete MFA enforcement on remote access paths (CWE-250, CWE-269, CWE-287)
Your security operations team measures detection-to-containment time in minutes or hours rather than seconds
Your organization operates in a sector historically targeted by espionage or financially motivated threat actors, where AI-augmented adversaries have the highest ROI incentive
Board Talking Points
Adversaries using AI can now move from initial system access to lateral spread in under 30 seconds, a speed that makes traditional patch-and-scan security programs structurally inadequate.
The board should direct management to assess whether the organization's current detection-to-containment capability can match this pace, and to report back within 60 days with a gap analysis and remediation plan.
Organizations that do not adapt their security operating model to continuous monitoring and machine-speed detection face materially higher breach probability and the associated regulatory, operational, and reputational costs.
Technical Analysis
The central argument in CrowdStrike's 2026 Global Threat Report, and the supporting blog series on frontier AI, is that AI is not merely automating existing attacker workflows, it is collapsing the temporal advantage defenders historically relied on.
Two data points anchor the analysis: a 42% increase in zero-days exploited before public disclosure, and a 27-second lateral movement breakout time.
Together, these figures describe an environment where the traditional vulnerability management lifecycle - scan, score by CVSS, prioritize, patch, verify - cannot close the gap between exposure and exploitation.
The MITRE ATT&CK techniques mapped to this threat pattern illustrate the full kill chain AI-augmented adversaries are accelerating. Initial access via public-facing application exploitation (T1190 ) or valid account abuse (T1078 ) is followed by rapid lateral movement (T1021 ) and privilege escalation (T1068 ). Credential brute-forcing (T1110 ) and use of stolen credentials or web session tokens (T1550 ) reduce the time needed to establish persistence. Critically, impair defenses (T1562 ) activity, disabling logging, EDR agents, or alerting pipelines, is appearing earlier in the attack sequence, suggesting adversaries are using AI to identify and neutralize detection capabilities before executing their primary objective. Tool and vulnerability weaponization acquisition (T1588.006 ) and exploitation of remote services (T1210 ) round out a pattern where every phase of the attack lifecycle is being accelerated.
The mapped CWEs (CWE-284, CWE-287, CWE-250, CWE-269) - improper access control, improper authentication, excessive privilege, and improper privilege management - describe the structural weaknesses AI-assisted attackers are most effectively targeting. These are editorial mappings to the threat pattern described, not tied to a specific CVE. Over-privileged service accounts, incomplete MFA enforcement, and excessive standing access provide the footholds that fast-moving adversaries exploit before defenders can respond.
One significant sourcing limitation applies to this story: secondary sources reference product names 'Anthropic Claude Mythos' and 'OpenAI GPT-5.4-Cyber,' which cannot be independently verified as commercial product identifiers. These product-level claims should be independently verified against official Anthropic and OpenAI documentation before use in formal communications. The core statistical claims - 89% increase in AI-enabled attacks, 42% increase in pre-disclosure zero-day exploitation, 27-second breakout time - are attributed to CrowdStrike's 2026 Global Threat Report and should be verified against that primary document before use in formal communications. This item is not tied to a specific CVE; the qualitative_rating of 'high' reflects editorial assessment of the threat pattern described.
Action Checklist IR ENRICHED
Triage Priority:
URGENT
Escalate to CISO and executive leadership immediately if detection-to-containment time measurement (Step 4) reveals D2C exceeds 30 minutes for lateral movement scenarios, if EDR coverage gaps (Step 2) expose more than 10% of internet-facing or Tier-0 assets without agent coverage, or if CISA issues a KEV entry for a vulnerability present in your environment while your organization remains on a weekly-or-slower scan cycle — any of these conditions indicates structural inability to respond within the AI-accelerated exploitation window documented in the CrowdStrike 2026 GTR.
1
Step 1: Assess exposure, audit whether your vulnerability management program operates on periodic scan cycles (weekly, monthly) rather than continuous monitoring; if so, quantify the gap between scan cadence and current mean time to exploit for your most common vulnerability classes
IR Detail
Preparation
NIST 800-61r3 §2 — Preparation: Establishing IR capability, tooling, and visibility baselines before adversary activity begins
NIST SI-4 (System Monitoring) — continuous monitoring requirement directly addresses the cadence gap this step quantifies
NIST RA-3 (Risk Assessment) — requires organizations to assess likelihood and impact, which must account for near-zero patch windows under AI-accelerated exploitation
NIST SI-2 (Flaw Remediation) — flaw remediation timelines must be reassessed when MTTE for common vulnerability classes now approaches or precedes vendor patch availability
CIS 7.1 (Establish and Maintain a Vulnerability Management Process) — explicitly requires documenting scan frequency and remediation SLAs; this step operationalizes a review of those documented commitments against current threat reality
CIS 7.2 (Establish and Maintain a Remediation Process) — risk-based remediation strategy must be recalibrated when 42% of zero-days are exploited pre-disclosure, making CVSS-score-at-disclosure an inadequate trigger
Compensating Control
For teams without a continuous scanning platform: deploy OpenVAS (Greenbone Community Edition) in authenticated scan mode on a 24-hour cron schedule targeting internet-facing assets and domain controllers; run `nmap -sV --script vulners -p 80,443,8080,8443,445,3389,22` nightly against your perimeter and pipe output to a diff script that alerts on new open ports or version changes. Use CISA's KEV catalog (downloaded via `curl https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json`) cross-referenced against your asset inventory in a spreadsheet to identify KEV-listed CVEs present in your environment — this approximates continuous prioritization without a SIEM.
Preserve Evidence
Before reconfiguring scan cadence, capture a point-in-time baseline: export your current vulnerability scanner's last full scan report with timestamps to establish the historical scan interval; pull CISA KEV JSON and record the `dateAdded` vs. `dueDate` delta for any CVEs matching your installed software to quantify how often exploitation precedes your scan window; document your scanner's authenticated vs. unauthenticated coverage ratio, as unauthenticated scans systematically miss privilege-escalation-class vulnerabilities (T1068) that AI-assisted exploit chains favor for rapid lateral movement.
2
Step 2: Review controls, verify MFA enforcement across all privileged and remote access paths (T1078, T1110); audit EDR agent coverage for gaps and confirm that impair-defenses detections (T1562) are alerting correctly; review service account privilege levels against least-privilege standards (CWE-250, CWE-269)
IR Detail
Preparation
NIST 800-61r3 §2 — Preparation: Ensuring detection and defensive controls are operational and correctly tuned before an AI-accelerated intrusion attempt occurs
NIST AC-2 (Account Management) — service account enumeration and privilege review maps directly to CWE-269 (Improper Privilege Management); accounts with excessive rights are the primary escalation path in T1078 abuse
NIST IA-5 (Authenticator Management) — MFA enforcement verification across privileged and remote paths addresses T1110 (Brute Force) and T1078 (Valid Accounts), both of which are accelerated by AI-driven credential stuffing and password spraying at machine speed
NIST SI-4 (System Monitoring) — T1562 (Impair Defenses) detection alerting verification is a direct SI-4 implementation check; if EDR agent tampering goes undetected, the 27-second lateral movement window becomes irrelevant because containment telemetry disappears
NIST IR-4 (Incident Handling) — EDR coverage gap audit is preparation-phase IR-4 work: you cannot execute containment at 27-second breakout speed if agent gaps leave blind spots on lateral movement paths
CIS 6.3 (Require MFA for Externally-Exposed Applications) — direct control for the T1078/T1110 MFA verification task
CIS 6.5 (Require MFA for Administrative Access) — privileged path MFA audit; AI-enabled adversaries specifically target unprotected admin accounts because compromising one collapses the kill chain
CIS 5.4 (Restrict Administrator Privileges to Dedicated Administrator Accounts) — service account least-privilege review operationalizes this safeguard against CWE-250 and CWE-269
Compensating Control
MFA gap audit without enterprise IAM tooling: run `Get-ADUser -Filter * -Properties 'msDS-SupportedEncryptionTypes','ServicePrincipalName','PasswordLastSet' | Where-Object {$_.ServicePrincipalName -ne $null}` to enumerate service accounts, then cross-reference against your MFA enrollment list manually. For EDR coverage gaps without a fleet management console, deploy osquery with the CIS osquery pack and query `SELECT * FROM processes WHERE name = 'MsMpEng.exe' OR name = 'CSFalconService.exe'` across endpoints via scheduled task; missing results identify uncovered hosts. For T1562 alerting verification, deploy the Sigma rule `windows_defender_tamper` (SigmaHQ detection-rules repo) and test by temporarily disabling real-time protection on a lab host to confirm alert fires.
Preserve Evidence
Before auditing controls, collect: Windows Security Event Log Event ID 4625 (failed logons) and 4648 (explicit credential use) from domain controllers for the trailing 30 days to establish T1110 baseline activity volume — AI-driven credential attacks will appear as statistically anomalous spikes against this baseline; CrowdStrike Falcon (or equivalent EDR) agent heartbeat logs showing last check-in time per host to identify stale/missing agents before the audit modifies any configuration; Active Directory replication metadata (`repadmin /showrepl`) to identify service accounts with `PasswordNeverExpires` and `DontRequirePreauth` flags set, as these are the highest-value targets for AI-accelerated Kerberoasting (T1558.003) chained into T1078.
3
Step 3: Update threat model, add AI-accelerated exploitation as a named threat pattern in your threat register; specifically document the assumption that a meaningful patch window no longer exists for internet-facing systems and high-value internal assets; map to T1190, T1068, T1021, T1562
IR Detail
Preparation
NIST 800-61r3 §2 — Preparation: Updating threat models and IR plans to reflect current adversary capability, specifically the elimination of the assumed patch window documented in CrowdStrike's 2026 Global Threat Report
NIST RA-3 (Risk Assessment) — threat model update is a direct RA-3 activity; the specific update required here is adding 'AI-accelerated exploitation with near-zero patch window' as a named likelihood driver that elevates risk ratings for internet-facing assets independent of CVSS score
NIST RA-5 (Vulnerability Monitoring and Scanning) — the threat model must now reflect that RA-5 scan frequency is no longer sufficient as a primary risk reduction control; this updates the compensating control assumptions embedded in existing risk assessments
NIST IR-8 (Incident Response Plan) — IR plan must be updated to reflect T1190/T1068 exploitation scenarios where the window between disclosure and active exploitation is measured in hours, not weeks; playbooks built on 'patch within 30 days' SLAs require immediate revision
NIST SI-5 (Security Alerts, Advisories, and Directives) — AI-accelerated exploitation changes the operationalization of SI-5: advisories can no longer be queued for weekly triage; the threat model update must encode a near-real-time advisory response workflow
CIS 7.1 (Establish and Maintain a Vulnerability Management Process) — the vulnerability management process documentation must be revised to name AI-assisted exploitation as a process assumption that invalidates periodic-scan-only approaches
Compensating Control
For teams maintaining threat registers in spreadsheets rather than GRC platforms: add a column `AI_Accel_Applicable` (boolean) to your existing threat register and mark true for any threat scenario involving T1190, T1068, T1021, or T1562; add a second column `Patch_Window_Assumption_Days` and update internet-facing asset rows to 0 (no assumed window) per CrowdStrike 2026 GTR findings. For ATT&CK technique mapping without a commercial threat intelligence platform, use the MITRE ATT&CK Navigator (free, browser-based at attack.mitre.org/resources/attack-navigator/) to build a layer file for T1190, T1068, T1021.001, T1021.002, and T1562.001, then export as JSON to attach to your threat register entry.
Preserve Evidence
Before finalizing threat model updates, preserve the current state as a versioned baseline: export your existing threat register with timestamps and current risk ratings for all internet-facing asset threat scenarios; document current patch SLA commitments from your vulnerability management policy (the specific number of days); pull CISA KEV `dateAdded` vs. NVD `publishedDate` delta for the last 90 days of entries to empirically quantify how often exploitation precedes or immediately follows disclosure in your relevant software categories — this data substantiates the threat model assumption change with evidence rather than vendor claims alone.
4
Step 4: Communicate findings, brief leadership using the 27-second breakout time and 42% pre-disclosure zero-day figures as concrete anchors; frame the business question as: what is our detection-to-containment time, and does it beat 27 seconds for lateral movement scenarios
IR Detail
Post-Incident
NIST 800-61r3 §4 — Post-Incident Activity: Lessons learned and capability gap reporting to leadership; this step applies post-incident communication discipline to a proactive capability assessment, using published threat data as a surrogate for observed incident data
NIST IR-6 (Incident Reporting) — while this is a proactive brief rather than an active incident report, IR-6's requirement to communicate incident-relevant information to appropriate organizational personnel directly applies; leadership needs the 27-second breakout metric to make resourcing decisions before the incident occurs
NIST IR-8 (Incident Response Plan) — the brief should result in documented updates to the IR plan authorizing faster containment actions (network isolation, account lockout) at lower confidence thresholds, given that waiting for confirmation is structurally incompatible with 27-second lateral movement
NIST RA-3 (Risk Assessment) — the 42% pre-disclosure zero-day figure and 89% YoY increase in AI-enabled attacks are quantitative inputs to RA-3 likelihood determinations; the brief should frame these as risk assessment data points requiring plan updates, not just awareness
CIS 7.2 (Establish and Maintain a Remediation Process) — the business question framing ('does our detection-to-containment time beat 27 seconds') is a direct challenge to existing remediation SLA assumptions that CIS 7.2 requires be documented and reviewed
Compensating Control
To calculate actual detection-to-containment time without a SIEM: pull the five most recent security incidents from your ticketing system, calculate elapsed time between 'alert created' and 'host isolated/account locked' timestamps manually, and present the median as your current D2C time against the 27-second benchmark. For teams with no prior incident history, run a tabletop exercise specifically scripted around a 27-second lateral movement scenario (attacker compromises one host, moves to domain controller within 27 seconds) using the CISA Tabletop Exercise Packages (CTEPs) framework to generate an estimated response time for the brief.
Preserve Evidence
Before the leadership brief, gather your own organizational data to contextualize vendor figures: pull EDR or Windows Event Log mean time between alert generation (Event ID 1102 — audit log cleared, as a proxy for attacker anti-forensics activity, or EDR alert timestamps) and analyst acknowledgment from the last 90 days; document the current number of internet-facing systems without continuous monitoring coverage; if CrowdStrike Falcon is deployed, pull the 'Time to Detect' and 'Time to Investigate' metrics from the Falcon console's Incident Workbench for recent detections — these become your organization-specific counterpart to the 27-second GTR figure and are more persuasive to leadership than vendor benchmarks alone.
5
Step 5: Monitor developments and verify claims. Track CrowdStrike's 2026 Global Threat Report for release of full statistical methodology and attack campaign attribution. Prioritize independent corroboration of the AI-enabled attack metrics from CISA, NIST exploitation tracking, or peer vendor threat intelligence before using figures in board-level communications.
IR Detail
Post-Incident
NIST 800-61r3 §4 — Post-Incident Activity: Continuous improvement through integration of updated threat intelligence; specifically, monitoring for corroboration of AI-acceleration metrics maps to the lessons-learned and threat model refinement activities NIST 800-61r3 assigns to this phase
NIST SI-5 (Security Alerts, Advisories, and Directives) — SI-5 requires receiving and acting on security advisories from external organizations on an ongoing basis; this step operationalizes SI-5 for the specific case of AI-acceleration threat intelligence, naming CISA and NVD as the authoritative external sources to monitor
NIST IR-5 (Incident Monitoring) — tracking the evolution of AI-enabled attack metrics over time is an IR-5 activity; the threat register entry created in Step 3 must be updated as corroborating data arrives from CISA KEV, NVD exploitation flags, and peer vendor reports
NIST DE.AE-07 (Cyber threat intelligence integrated into adverse event analysis) — this NIST 800-61r3 CSF-aligned recommendation directly requires integrating up-to-date CTI into adverse event analysis; monitoring for corroboration of the 89% AI-attack increase and 27-second breakout metrics is how you keep that CTI current
CIS 7.1 (Establish and Maintain a Vulnerability Management Process) — the vulnerability management process must be updated when corroborating data changes the empirical basis for patch window assumptions; this monitoring step creates the trigger mechanism for that update
Compensating Control
For teams without a commercial threat intelligence feed: subscribe to CISA's free advisories via RSS (https://www.cisa.gov/cybersecurity-advisories — verify URL at time of use) and set a calendar reminder to cross-reference NVD's exploitation data (`https://nvd.nist.gov/vuln/search` with `hasKev=1` filter — verify URL at time of use) monthly; create a shared document tracking the following metrics over time: CISA KEV entries per month, median days-to-KEV-listing from NVD publish date, and any CISA/NSA joint advisory language referencing AI-assisted exploitation. Use the MITRE ATT&CK changelog RSS feed to track additions of AI-assisted technique sub-procedures as independent corroboration of the threat pattern.
Preserve Evidence
Establish a monitoring baseline before tracking begins: snapshot the current CISA KEV catalog entry count and the current NVD count of CVEs flagged with EPSS scores above 0.9 (indicating high exploitation probability); record the current version and publication date of the CrowdStrike 2026 Global Threat Report preview material being referenced; document which peer vendor threat reports (Mandiant M-Trends, Verizon DBIR, Microsoft MSTIC annual report) are currently in your intelligence review cycle so that additions can be tracked as corroborating sources when AI-acceleration metrics appear in those independent publications.
Recovery Guidance
Recovery in the context of this threat story is programmatic rather than system-specific: after implementing continuous monitoring and tightening MFA and EDR coverage per Steps 1 and 2, verify recovery by re-running the detection-to-containment time calculation from Step 4 against a controlled tabletop or purple team exercise scripted to the 27-second lateral movement scenario, confirming D2C has measurably decreased. Monitor your vulnerability management SLA compliance weekly for 90 days post-implementation to confirm the shift from periodic to continuous scanning is operationally sustained and not reverting under workload pressure. Treat any future CISA KEV entry for software in your environment as a live test of the updated program: measure time from KEV publication to confirmed remediation or compensating control deployment and track this as a KPI to demonstrate improvement to leadership.
Key Forensic Artifacts
Windows Security Event Log — Event ID 4648 (Logon with explicit credentials) and 4624 Type 3 (Network logon) on domain controllers and Tier-0 servers: AI-accelerated lateral movement via T1021 will produce a statistically anomalous burst of these events within a compressed timeframe (seconds to low minutes) rather than the distributed pattern of human-speed lateral movement; baseline normal hourly rates before any incident to make the anomaly detectable
EDR process tree telemetry (CrowdStrike Falcon Process Timeline or equivalent) for processes spawned by internet-facing service accounts: T1190 exploitation followed by T1068 privilege escalation will produce characteristic parent-child process relationships (e.g., web server process spawning cmd.exe, powershell.exe, or mshta.exe) that represent the initial post-exploitation foothold before AI-assisted lateral movement begins at T1021
CrowdStrike Falcon Incident Workbench 'Impair Defenses' detection alerts (T1562): AI-assisted exploit chains specifically target EDR agent disable/tamper as an early kill-chain step to eliminate telemetry before lateral movement; the presence or conspicuous absence of T1562 alerts during a suspected intrusion window is itself a forensic indicator
CISA KEV catalog delta log — a versioned daily snapshot of the KEV JSON feed cross-referenced against your asset inventory: when an exploitation event is investigated, the timestamp of KEV listing relative to your last successful scan of the affected asset quantifies exactly how large your exposure window was, providing concrete forensic evidence of the patch-window gap this threat story describes
Active Directory Kerberos TGS request logs (Event ID 4769 on domain controllers) filtered for service accounts with weak encryption types (RC4, 0x17): AI-accelerated Kerberoasting (T1558.003) as a precursor to T1078 valid-account abuse will produce high-volume TGS requests for service accounts in a compressed window; these logs must be captured before any credential rotation activity in Step 2 to preserve the pre-remediation forensic baseline
Detection Guidance
Given the attack pattern described, detection focus should shift from indicator-matching to behavior sequencing at machine speed.
Specific areas to instrument and hunt:
- Lateral movement velocity: Alert on authentication events or remote service connections (T1021 ) occurring within seconds of an initial access event or privilege escalation.
A 27-second breakout time means dwell-time-based detection thresholds calibrated for minutes or hours will miss these sequences entirely.
- Defense impairment sequencing (T1562 ): Hunt for EDR agent stops, log forwarding interruptions, or firewall rule modifications occurring within the first minutes of a new session or elevated process.
Early impairment activity is a strong behavioral indicator of AI-assisted or scripted attack execution.
- Privilege escalation followed by lateral movement (T1068 → T1021 ): Correlate local privilege escalation events with subsequent remote service connections, particularly to high-value targets such as domain controllers, backup infrastructure, or payment systems.
- Valid account abuse at speed (T1078 , T1550 ): Monitor for credential reuse or session token replay across multiple systems in compressed time windows. AI-assisted credential stuffing (T1110 ) will produce authentication attempt volumes and velocities inconsistent with human behavior.
- Pre-disclosure zero-day exploitation (T1190 ): Because 42% of zero-days in this reporting period were exploited before public disclosure, signature-based detection on CVE identifiers will fail. Prioritize behavioral detection on post-exploitation activity rather than exploit signatures.
- Log sources to prioritize: authentication logs (Windows Security Event ID 4624, 4648, 4625), EDR process telemetry, network flow data for east-west lateral movement, and endpoint integrity monitoring for agent status changes.
Security operations teams should review detection rule coverage for the mapped ATT&CK techniques, particularly T1562 (impair defenses) and T1068 (exploitation for privilege escalation), and validate that behavioral analytics modules are active and tuned to catch AI-assisted attack sequences.
Indicators of Compromise (1)
Export as
Splunk SPL
KQL
Elastic
Copy All (1)
1 tool
Type Value Enrichment Context Conf.
⚙ TOOL
Pending — refer to CrowdStrike 2026 Global Threat Report for published indicators
CrowdStrike's 2026 Global Threat Report is cited as the primary source for AI-enabled attack statistics; specific IOCs, tool hashes, and campaign indicators associated with documented AI-assisted intrusions may be published in that report or its accompanying intelligence releases
LOW
Platform Playbooks
Microsoft Sentinel / Defender
CrowdStrike Falcon
AWS Security
🔒
Microsoft 365 E3
3 log sources
Basic identity + audit. No endpoint advanced hunting. Defender for Endpoint requires separate P1/P2 license.
🛡
Microsoft 365 E5
18 log sources
Full Defender suite: Endpoint P2, Identity, Office 365 P2, Cloud App Security. Advanced hunting across all workloads.
🔍
E5 + Sentinel
27 log sources
All E5 tables + SIEM data (CEF, Syslog, Windows Security Events, Threat Intelligence). Analytics rules, playbooks, workbooks.
IOC Detection Queries (1)
Known attack tool — NOT a legitimate system binary. Any execution is suspicious.
KQL Query Preview
Read-only — detection query only
// Threat: Frontier AI Is Closing the Exploit Window: Traditional Vulnerability Management
// Attack tool: Pending — refer to CrowdStrike 2026 Global Threat Report for published indicators
// Context: CrowdStrike's 2026 Global Threat Report is cited as the primary source for AI-enabled attack statistics; specific IOCs, tool hashes, and campaign indicators associated with documented AI-assisted intr
DeviceProcessEvents
| where Timestamp > ago(30d)
| where FileName =~ "Pending — refer to CrowdStrike 2026 Global Threat Report for published indicators"
or ProcessCommandLine has "Pending — refer to CrowdStrike 2026 Global Threat Report for published indicators"
or InitiatingProcessCommandLine has "Pending — refer to CrowdStrike 2026 Global Threat Report for published indicators"
| project Timestamp, DeviceName, FileName, FolderPath,
ProcessCommandLine, AccountName, AccountDomain,
InitiatingProcessFileName, InitiatingProcessCommandLine
| sort by Timestamp desc
MITRE ATT&CK Hunting Queries (5)
Sentinel rule: Web application exploit patterns
KQL Query Preview
Read-only — detection query only
CommonSecurityLog
| where TimeGenerated > ago(7d)
| where DeviceVendor has_any ("PaloAlto", "Fortinet", "F5", "Citrix")
| where Activity has_any ("attack", "exploit", "injection", "traversal", "overflow")
or RequestURL has_any ("../", "..\\\\", "<script", "UNION SELECT", "\${jndi:")
| project TimeGenerated, DeviceVendor, SourceIP, DestinationIP, RequestURL, Activity, LogSeverity
| sort by TimeGenerated desc
Sentinel rule: Security tool tampering
KQL Query Preview
Read-only — detection query only
DeviceProcessEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine has_any (
"Set-MpPreference", "DisableRealtimeMonitoring",
"net stop", "sc stop", "sc delete", "taskkill /f",
"Add-MpPreference -ExclusionPath"
)
| where ProcessCommandLine has_any ("defender", "sense", "security", "antivirus", "firewall", "crowdstrike", "sentinel")
| project Timestamp, DeviceName, ProcessCommandLine, AccountName, InitiatingProcessFileName
| sort by Timestamp desc
Sentinel rule: Sign-ins from unusual locations
KQL Query Preview
Read-only — detection query only
SigninLogs
| where TimeGenerated > ago(7d)
| where ResultType == 0
| summarize Locations = make_set(Location), LoginCount = count(), DistinctIPs = dcount(IPAddress) by UserPrincipalName
| where array_length(Locations) > 3 or DistinctIPs > 5
| sort by DistinctIPs desc
Sentinel rule: Password spray / brute force
KQL Query Preview
Read-only — detection query only
SigninLogs
| where TimeGenerated > ago(7d)
| where ResultType in ("50126", "50053", "50055")
| summarize FailedAttempts = count(), DistinctUsers = dcount(UserPrincipalName) by IPAddress, bin(TimeGenerated, 1h)
| where FailedAttempts > 10 or DistinctUsers > 5
| sort by FailedAttempts desc
Sentinel rule: Lateral movement via RDP / SMB / WinRM
KQL Query Preview
Read-only — detection query only
DeviceNetworkEvents
| where Timestamp > ago(7d)
| where RemotePort in (3389, 5985, 5986, 445, 135)
| where LocalIP != RemoteIP
| summarize ConnectionCount = count(), TargetDevices = dcount(RemoteIP) by DeviceName, InitiatingProcessFileName
| where ConnectionCount > 10 or TargetDevices > 3
| sort by TargetDevices desc
No actionable IOCs for CrowdStrike import (benign/contextual indicators excluded).
No hard IOCs available for AWS detection queries (contextual/benign indicators excluded).
Compliance Framework Mappings
T1210
T1550
T1190
T1562
T1078
T1110
+3
AC-6
SC-7
SI-2
CA-8
RA-5
SI-7
+12
6.1
6.2
6.3
6.4
6.5
5.4
+3
MITRE ATT&CK Mapping
T1210
Exploitation of Remote Services
lateral-movement
T1550
Use Alternate Authentication Material
defense-evasion
T1190
Exploit Public-Facing Application
initial-access
T1562
Impair Defenses
defense-evasion
T1078
Valid Accounts
defense-evasion
T1110
Brute Force
credential-access
T1021
Remote Services
lateral-movement
T1068
Exploitation for Privilege Escalation
privilege-escalation
T1588.006
Vulnerabilities
resource-development
Guidance Disclaimer
