Monitoring & Logging (ML) - Endpoints Assessment

Assessment questionnaire for measuring maturity. Answer each question honestly based on current, implemented practices.

v3.0 canonical: This questionnaire is authored against the v3.0 source-of-truth one-pager ../practices/ML-Endpoints-OnePager.md and the §10.2 priority compliance map in ../HAIAMM-v3.0-Framing.md. Through-lines: EU AI Act Art. 12 and Art. 50 transparency / disclosure audit trail · GDPR Art. 30 records of processing for customer-facing AI · ISO/IEC 42001 AIMS operational evidence.

Monitoring & Logging (ML) - Endpoints Domain

HAIAMM Assessment Questionnaire v3.0

Practice: Monitoring & Logging (ML) Domain: Endpoints Purpose: Assess organizational maturity in establishing the per-archetype logging baseline for AI/HAI endpoints, operating a high-signal detection set targeting TA-Endpoints threats, and producing the evidence trail that satisfies EU AI Act Arts. 12 and 50, GDPR Art. 30, and ISO/IEC 42001 AIMS requirements on demand.

Instructions

Answer each question honestly based on current, implemented practices (not plans or aspirations)
Each question has two components: Evidence (what you have done) and Outcome Metrics (how well it is working)
Scoring uses 4 tiers: Fully Mature (1.0), Implemented (0.67), Partial (0.33), Not Implemented (0.0)
Answer progressively, Complete all Level 1 questions before Level 2
Level progression, Achieve ALL questions at a lower level before advancing
Baseline first, Record current metric values before setting targets

Scoring Methodology

Score	Label	Criteria
1.0	Fully Mature	Evidence complete + ≥3 outcome metrics meet targets
0.67	Implemented	Evidence complete + 2 outcome metrics meet targets
0.33	Partial	Evidence partially complete + <2 outcome metrics meet targets
0.0	Not Implemented	No evidence of the practice

Level score = average of the three question scores for that level. Overall ML-Endpoints score = weighted average: L1 × 0.5 + L2 × 0.3 + L3 × 0.2.

Maturity Level 1

Objective: Establish the per-archetype logging baseline, operate a small high-signal detection set targeting the top TA-Endpoints threats, and produce an on-demand evidence trail that satisfies EU AI Act Arts. 12 and 50, GDPR Art. 30, and ISO/IEC 42001 AIMS requirements within a published SLA.

Question 1: Per-Archetype Logging Baseline

Q1.1: Has a per-archetype logging baseline been published specifying the minimum event schema, fields, retention window, and export path for each AI/HAI endpoint archetype in the SM-Endpoints inventory, and has compliance of each production archetype been measured against it within the last quarter?

Evidence Required: - [ ] Published baseline specifying minimum events per archetype: AI assistant/copilot on managed endpoint (session/paste-block-paste-allow DLP-decision/tool-call/admin-audit), browser-based AI tool (extension-install/extension-uninstall/extension-permission-grant/DLP-decision/backend-SSO), chatbot/conversational UI (customer-interaction with disclosure-shown flag/output-filter/abuse-detection), multi-modal AI interface (input with content hash/output-safety-filter/cross-modal-consistency), AI-augmented productivity SaaS-AI (admin-audit AI-feature enablement/per-feature usage/DLP-decision), mobile AI app (app-launch/permission-grant/local-model-integrity/on-device-action), edge AI device (boot-attestation/physical-tamper/uplink/remote-disable), and admin-audit + identity events cross-archetype - [ ] Chatbot interaction events: disclosure-shown flag (yes/no, disclosure-template-version) and PII redaction recorded per session, not raw customer message content for regulated data - [ ] Edge AI device: boot-attestation result, firmware version, and sealed PCR values hash included in boot-attestation event - [ ] Retention window configured per §10.2 priority compliance map: EU AI Act Art. 12 high-risk logs ≥6 months; GDPR Art. 30 per data-class and purpose; HIPAA ≥6 years where applicable; COPPA for children-facing endpoints; FERPA for educational endpoints; sector mobile-banking where applicable; longest governs per archetype - [ ] Export path (JSON or structured format) tested at least annually; export SLA ≤24 hours documented - [ ] Compliance audit within the last quarter; gaps on IM-Endpoints backlog with named owner

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | % production AI/HAI endpoint archetypes meeting per-archetype logging baseline | % | % | ≥90% | ☐ | | | % production endpoint archetypes with retention meeting longest applicable regulation | % | % | 100% | ☐ | | | Export SLA test result, on-demand pull time (quarterly drill) | h | h | ≤24h | ☐ | | | Archetype-baseline gap count on IM-Endpoints backlog with named owner | ___ | ___ | 0 unowned gaps | ☐ | |

Metric Collection Guidance: - Logging baseline compliance: Cross-reference each production endpoint archetype against the published baseline checklist. Count archetypes meeting all required fields divided by total archetypes. Source: logging configuration audit × SM-Endpoints inventory. - Retention compliance: Compare configured retention against the longest applicable regulation from §10.2 plus sector overlays for each archetype. Source: retention policy audit. - Export SLA: Time the quarterly deployer-duty drill. Measure request trigger to assembled package. Source: drill records. - Gap count: Count IM-Endpoints open findings tagged "logging-baseline-gap" with named owner. Source: IM-Endpoints backlog.

Answer: - ☐ Fully Mature (Evidence complete + ≥3 metrics meet targets) - ☐ Implemented (Evidence complete + 2 metrics meet targets) - ☐ Partial (Evidence partially complete + <2 metrics meet targets) - ☐ Not Implemented (No per-archetype logging baseline)

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 2: High-Signal Detection Set

Q1.2: Is a high-signal detection set of ≤12 detections active, each tied to a TA-Endpoints archetype threat, including regulated-data paste-attempt, customer-data egress via AI assistant, unsanctioned browser extension, SaaS-AI shadow-enablement, mobile-app integrity failure, edge tamper, Art. 50 disclosure suppression, chatbot abuse at scale, and cross-tenant SaaS-AI exposure, with false-positive rates tracked and monthly tuning reviews occurring?

Evidence Required: - [ ] Detection registry: each entry includes owner, detection query, SLA, archetype tag, HAI-TTP tag, last-tuned date, and false-positive rate - [ ] Regulated-data paste-attempt blocked (high volume) detection active: DLP block events for regulated-data paste into AI tool prompt fields exceeding threshold per user/endpoint in rolling window; routes to IM-Endpoints and EG-Endpoints (EA TTP) - [ ] Customer-data egress via AI assistant detection active: DLP allow event where data class is regulated customer data and destination is an AI tool endpoint; single allow event at this classification routes immediately as High-severity (ATLAS TA0013 / AGH TTP) - [ ] Unsanctioned browser extension installed detection active: extension-install event where extension ID not in SM-Endpoints browser-extension allowlist (EA TTP) - [ ] SaaS-AI feature enabled tenant-wide without intake detection active: SaaS-admin audit event recording new AI feature enablement without matching SM-Endpoints intake approval (EA TTP) - [ ] Mobile-app local-model integrity failure detection active: local-model integrity event where integrity check result = fail; any single failure routes to IM-Endpoints - [ ] Edge-device tamper / attestation failure detection active: boot-attestation event with result fail, or physical-tamper event with tamper sensor triggered; routes immediately to IM-Endpoints for remote-disable evaluation - [ ] Chatbot EU AI Act Art. 50 disclosure suppression detection active: customer-interaction event where disclosure-shown flag is false at session start for a scope where Art. 50 disclosure is required - [ ] Chatbot abuse-pattern at scale detection active: abuse-detection events of type jailbreak-attempt or prompt-injection-attempt above threshold per session/time window (ATLAS TA0008 / AGH TTP) - [ ] Cross-tenant data exposure via SaaS-AI feature detection active: DLP-decision event for SaaS-AI data flows where data scope includes a tenant or org-unit boundary not declared in the feature's SM-Endpoints intake record; routes as Critical (TM TTP) - [ ] Monthly tuning review log with review dates and changes

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | Total active detections in detection registry | ___ | ___ | ≤12 | ☐ | | | Median detection-to-IM-Endpoints-ticket time for Critical-tier archetypes | h | h | ≤1h | ☐ | | | % detections with FP rate tracked and last-tuned date ≤30 days | % | % | 100% | ☐ | | | Monthly tuning reviews completed (last 12 months) | /12 | /12 | 12/12 | ☐ | |

Metric Collection Guidance: - Active detections: Count entries in detection registry with status = active. Source: detection registry. - Detection-to-ticket time: Measure from detection alert to IM-Endpoints ticket creation for Critical-tier archetype detections. Source: alert → ticket telemetry (P50). - FP rate tracked: Count detections with FP rate populated and last-tuned date ≤30 days divided by total active detections. Source: detection tuning log. - Monthly reviews: Count monthly tuning review sessions with documented output in the last 12 calendar months. Source: detection tuning log.

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 3: Deployer-Duty Evidence Trail

Q1.3: Has the evidence trail for EU AI Act Arts. 12 and 50, GDPR Art. 30, and ISO/IEC 42001 AIMS been wired to the ML-Endpoints log store, including sector overlays where applicable, and has a quarterly deployer-duty drill confirmed evidence assembles within the ≤24-hour SLA?

Evidence Required: - [ ] EU AI Act Art. 12 wiring: for every endpoint archetype assessed as Annex III high-risk or decision-affecting, interaction/output-filter/admin-audit events captured at required retention; deployer-duty evidence view produced per archetype - [ ] EU AI Act Art. 50 wiring: for every customer-facing chatbot and multi-modal AI interface, the disclosure-shown flag and disclosure-template-version in the customer-interaction log constitute the Art. 50 audit trail; retention confirmed at required window; export path tested annually - [ ] GDPR Art. 30 wiring: DLP-decision/session/admin-audit events with principal identity and data-class tag for each personal-data-processing endpoint archetype link to Art. 30 record; log-store retention linked per archetype - [ ] ISO/IEC 42001 AIMS wiring: admin-audit events for AI-feature enablement changes, DLP-rule changes, rate-limit configuration changes, and integrity-check results identified as AIMS operational records; gaps on IM-Endpoints findings - [ ] Sector overlay compliance: COPPA for children-facing endpoints (interaction logs do not contain PII for minors in clear-text; age-gate decision events); FERPA for educational endpoints; sector mobile-banking where applicable - [ ] Quarterly deployer-duty drill records showing: archetype selected, drill start time, package assembly time, gaps found, disposition to IM-Endpoints

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | Quarterly deployer-duty drills completed (last 4 quarters) | /4 | /4 | 4/4 | ☐ | | | Evidence assembly time in most recent drill | h | h | ≤24h | ☐ | | | % customer-facing chatbot/multi-modal archetypes with Art. 50 audit trail documented | % | % | 100% | ☐ | | | Sector overlay retention windows confirmed where applicable | ___ | ___ | all applicable confirmed | ☐ | |

Metric Collection Guidance: - Drill completion: Count quarterly drill sessions in the last 4 quarters with documented output. Source: drill records. - Assembly time: Record most recent drill's assembly time. Source: drill records. - Art. 50 audit trail: Count customer-facing chatbot and multi-modal archetypes with disclosure-shown flag event schema instrumented and Art. 50 audit trail documented divided by total such archetypes. Source: evidence registry × SM-Endpoints inventory. - Sector overlay confirmation: Verify retention configuration for each in-scope sector overlay (COPPA, FERPA, mobile-banking) against the applicable regulatory requirement. Source: retention policy audit.

Evidence Location: _____ Validation Date: ____ Notes: ______

Maturity Level 2

Objective: Calibrate logging depth and detection set to the SM-Endpoints L2 risk-tier rubric; integrate with SIEM for cross-archetype correlation; and feed incident-driven and ST-Endpoints-driven detection updates into a continuous tuning loop.

Question 4: Tier-Calibrated Logging Depth

Q2.1: Is tier-calibrated logging depth applied per the SM-Endpoints L2 tier-treatment matrix, Critical-tier archetypes retaining full interaction-event and DLP-decision log corpora at the longest regulatory window, Low-tier archetypes receiving baseline only, and is this calibration automatically updated when an archetype is re-tiered (Critical re-tier within 14 days)?

Evidence Required: - [ ] Tier-treatment matrix applied: Critical = full interaction-event and DLP-decision log corpora (not hashes) at longest regulatory window + full admin-audit at maximum fidelity + all detections + anomaly detection baselines + log store partitioned; High = full interaction and DLP-decision events + standard admin-audit + core detections + anomaly baselines; Medium = interaction event hashes + standard admin-audit + shadow-AI emergence and baseline detections; Low = baseline schema only + shadow-AI emergence detection only - [ ] Re-tier update process: Critical re-tier logging depth updated within 14 days; other tiers within 30 days; evidence of last re-tier event with update timestamp - [ ] ML-Endpoints log store is primary source for PC-Endpoints compliance evidence bundles; ML-Endpoints logging-baseline validation element ≤30 days stale for Critical-tier archetypes

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | % Critical-tier endpoint archetypes with full interaction-event and DLP-decision log corpora retained at longest regulatory window | % | % | 100% | ☐ | | | % archetypes with logging depth matching current tier assignment | % | % | 100% | ☐ | | | ML-Endpoints logging-baseline validation freshness, Critical-tier (days) | d | d | ≤30d | ☐ | | | Critical re-tier events with logging depth updated within 14 days (last 4 quarters) | % | % | 100% | ☐ | |

Metric Collection Guidance: - Critical-tier full corpus: Audit log-store retention for each Critical-tier endpoint archetype. Confirm full interaction-event and DLP-decision corpus at longest regulatory window. Source: log-store retention audit × SM-Endpoints inventory. - Tier-match coverage: Compare current tier assignment to logging configuration per archetype. Source: SM-Endpoints inventory × logging configuration audit. - Validation freshness: Check date of most recent ML-Endpoints logging-baseline validation in PC-Endpoints evidence bundle per Critical-tier archetype. Source: evidence registry. - Re-tier compliance: For each Critical re-tier event in the last 4 quarters, measure days from re-tier decision to logging configuration update. Source: SM-Endpoints change log × logging audit trail.

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 5: SIEM Integration and Cross-Archetype Correlation

Q2.2: Is the SIEM ingesting ML-Endpoints log feeds with ≥3 cross-archetype correlation rules active, covering multi-archetype data-exfiltration chain, browser-extension to SaaS-AI lateral move, and chatbot abuse escalation chain, and is a quarterly detection tuning cycle operating from IM-Endpoints post-incident and ST-Endpoints inputs including external advisory updates?

Evidence Required: - [ ] SIEM ingesting all tier-appropriate ML-Endpoints log feeds; ingestion health monitored; no rule silent >90 days without investigation - [ ] Multi-archetype data-exfiltration chain rule active: regulated-data DLP-allow event on AI assistant correlates to matching data-class access event on a SaaS-AI feature from the same principal in the same session window fires correlated exfiltration-chain detection - [ ] Browser-extension to SaaS-AI lateral move rule active: unsanctioned-extension-install detection correlates to a SaaS-AI feature-enablement admin-audit event from the same principal within 24 hours - [ ] Chatbot abuse escalation chain rule active: chatbot abuse-pattern detection correlates to an unusual SSO sign-in to the AI provider management console from the same principal's device in the same time window - [ ] Quarterly detection review cycle records: IM-Endpoints post-incident input, ST-Endpoints finding input (disclosure-suppression tests, DLP bypass tests, chatbot abuse tests), external advisories (ATLAS endpoint techniques, OWASP MASVS, app-store flags, edge-device CVEs) reviewed quarterly; ≥1 net detection change per cycle - [ ] Monthly anomaly-baseline refresh for Critical and High-tier endpoint archetypes documented

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | Cross-archetype SIEM correlation rules active and verified (last 90 days) | ___ | ___ | ≥3 active | ☐ | | | Quarterly detection tuning cycles executed (last 4 quarters) | /4 | /4 | 4/4 | ☐ | | | % Critical/High-tier endpoint archetypes with anomaly-detection baselines established | % | % | ≥90% | ☐ | | | Anomaly-detection FP rate for Critical-tier (trend direction) | ___ | ___ | trending down | ☐ | |

Metric Collection Guidance: - Correlation rules active: Count SIEM rules with status = active; verify last-fired date ≤90 days or document no applicable events. Source: SIEM rule registry. - Tuning cycles: Count quarterly cycles with documented IM-Endpoints and ST-Endpoints feedback integration and ≥1 net change. Source: detection change log. - Anomaly baselines: Count Critical/High-tier endpoint archetypes with behavioral anomaly-detection baselines divided by total Critical/High-tier archetypes. Source: detection telemetry. - FP rate trend: Compare anomaly FP rate current quarter vs. prior quarter for Critical-tier endpoint archetypes. Source: alert telemetry.

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 6: Anomaly Detection on Endpoint-AI Behavioral Patterns

Q2.3: Are ≥90% of Critical/High-tier endpoint archetypes running anomaly-detection baselines, with behavioral profiles refreshed monthly and FP rates tracked and trending down, and is the ML-Endpoints logging-baseline validation element completing inside the ≤30-day staleness threshold for all Critical-tier archetypes in PC-Endpoints compliance evidence bundles?

Evidence Required: - [ ] Behavioral baselines established for Critical and High-tier endpoint archetypes: DLP-decision volume, session patterns, extension-install rate, SaaS-AI feature usage patterns, mobile-app integrity check results, edge attestation cadence, specific to each archetype - [ ] Monthly baseline refresh cadence honored; last-refresh date per archetype; seasonal usage patterns (holiday chatbot traffic, quarterly SaaS-AI feature rollout) accounted for in refresh cadence - [ ] FP rate tracked per detection; detections exceeding 20% FP reviewed at quarterly cycle; detections not firing TP in 90 days reviewed for retirement - [ ] PC-Endpoints compliance evidence bundle showing ML-Endpoints logging-baseline validation element ≤30 days for each Critical-tier archetype

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | % Critical/High-tier endpoint archetypes with anomaly-detection behavioral baselines | % | % | ≥90% | ☐ | | | % anomaly-detection baselines refreshed monthly, Critical-tier (last 3 months) | % | % | 100% | ☐ | | | Compliance evidence bundle ML-Endpoints validation freshness, Critical-tier (days) | d | d | ≤30d | ☐ | | | Shadow-AI endpoint discoveries initiated by ML-Endpoints detections (trend toward zero undetected) | ___ | ___ | trending down | ☐ | |

Metric Collection Guidance: - Baseline establishment: Count Critical/High-tier endpoint archetypes with documented behavioral baselines divided by total Critical/High-tier archetypes. Source: detection telemetry. - Refresh cadence: For each Critical-tier archetype baseline, verify last-refresh date ≤30 days. Source: anomaly baseline refresh records. - Evidence bundle freshness: Check ML-Endpoints logging-baseline validation date in PC-Endpoints evidence bundle per Critical-tier archetype. Source: evidence registry. - Shadow-AI discoveries: Count unsanctioned browser extension or SaaS-AI shadow-enablement findings initiated by ML-Endpoints detections by quarter. Source: IM-Endpoints backlog.

Evidence Location: _____ Validation Date: ____ Notes: ______

Maturity Level 3

Objective: Express detections as code with automated deployment; apply anomaly detection on endpoint-AI behavioral patterns; contribute anonymized detection signatures and telemetry schemas to CSA Endpoint AI Safety Initiative, OWASP MASVS, and sector ISACs.

Question 7: Detection-as-Code for Endpoints Domain

Q3.1: Are ≥90% of detections expressed as version-controlled, CI/CD-deployed code artifacts with automated test coverage against realistic synthetic endpoint-AI log data, and is detection coverage auto-verified for 100% of new or re-tiered SM-Endpoints inventory entries within 24 hours?

Evidence Required: - [ ] Detection registry showing ≥90% of active detections with source-control reference (repo, path, version) - [ ] Detection CI/CD pipeline: unit tests over synthetic endpoint-AI event log data including SaaS-AI admin-audit event formats, edge-device attestation events, mobile-AI integrity events, and browser extension install events; integration tests against log replay environment - [ ] Synthetic test data reflects realistic field combinations present in real DLP-bypass patterns and SaaS-AI admin-audit events after provider updates, not generic log events - [ ] Detection deployment via same change-management pipeline as AI/HAI endpoint configuration; no ad hoc SIEM console edits; detection changes reviewed before deployment - [ ] Automation verifying detection coverage on SM-Endpoints inventory change events within 24 hours; gap findings opened in IM-Endpoints automatically

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | % detections expressed as version-controlled, CI/CD-deployed code artifacts | % | % | ≥90% | ☐ | | | Detection coverage auto-verified on SM-Endpoints inventory change within 24h | % | % | 100% | ☐ | | | Detection CI/CD pipeline health, builds passing (last 90 days) | % | % | ≥95% | ☐ | | | Detection deployment lead time (merge to production) | h | h | <24h | ☐ | |

Metric Collection Guidance: - Detection-as-code coverage: Count detections with source-control reference divided by total active detections. Source: detection registry × source control. - Auto-verification: For each SM-Endpoints inventory change event in the last quarter, verify automated gap check triggered within 24 hours. Source: automation telemetry. - Pipeline health: Count CI/CD detection pipeline builds with result = success divided by total builds in last 90 days. Source: CI/CD telemetry. - Lead time: Measure time from detection code merge approval to production deployment. Source: CI/CD deployment records.

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 8: Anomaly Detection on Endpoint-AI Behavioral Patterns at Scale

Q3.2: Are ≥90% of Critical/High-tier endpoint archetypes running anomaly detection on endpoint-AI behavioral patterns, with anomaly models retrained monthly on production log data, model versions tracked in the ML-Endpoints model registry, and anomaly alerts feeding the IM-Endpoints backlog through the same detection-to-ticket pipeline?

Evidence Required: - [ ] DLP-decision sequence anomaly model active: sessions with DLP-decision sequence (block frequency, data-class distribution, AI tool context pattern) that is a statistical outlier from normal user sessions, attacker data-staging, pre-exfiltration paste patterns, policy-bypass probe sequences - [ ] Extension-install behavior anomaly model active: install patterns (frequency, permission profile, install timing relative to DLP events) outside the baseline for the endpoint cohort, signals potential insider or compromised-account extension deployment - [ ] SaaS-AI feature-usage anomaly model active: feature-usage events whose data-scope-access distribution shifts from baseline on a rolling window, potential cross-tenant access or unusual bulk-data access - [ ] Edge-device attestation cadence anomaly model active: devices with attestation event cadence, PCR-value-change pattern, or uplink-volume distribution deviating from device cohort baseline, signals firmware tampering or replay-attack preparation - [ ] Mobile-app integrity failure cluster model active: cluster of local-model integrity failures across a device cohort within a time window, signals coordinated model-swap attempt or compromised distribution channel - [ ] Anomaly models retrained monthly; training data excludes attacker-session logs from past incidents; model versions tracked in ML-Endpoints model registry - [ ] Anomaly model alerts route to IM-Endpoints through same detection-to-ticket pipeline as rule-based detections; severity tagged to archetype's tier

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | % Critical/High-tier endpoint archetypes with anomaly detection active on endpoint-AI behavioral patterns | % | % | ≥90% | ☐ | | | Anomaly model retraining cadence honored (last 6 months) | /6 | /6 | 6/6 monthly | ☐ | | | % anomaly model alerts routed to IM-Endpoints within ≤1h of detection | % | % | ≥95% | ☐ | | | True-positive incidents surfaced first by anomaly detection (vs. rule-based), trend | ___ | ___ | trending up | ☐ | |

Metric Collection Guidance: - Anomaly detection coverage: Count Critical/High-tier endpoint archetypes with active anomaly models across the five behavioral dimensions divided by total Critical/High-tier archetypes. Source: anomaly model registry. - Retraining cadence: Count months in the last 6 where a new anomaly model version was registered per archetype. Source: ML-Endpoints model registry. - Alert routing: Count anomaly alerts reaching an IM-Endpoints ticket within 1 hour divided by total anomaly alerts. Source: alert → ticket telemetry. - TP trend: Count incidents where first detection signal was an anomaly model alert by quarter. Source: incident retrospectives.

Evidence Location: _____ Validation Date: ____ Notes: ______

Question 9: Industry Contribution, Endpoint Detection Signatures and Schemas

Q3.3: Has the program contributed ≥2 telemetry-standard artifacts per year to CSA Endpoint AI Safety Initiative or equivalent, and ≥12 anonymized detection signatures per year to sector ISACs, with OWASP MASVS contributions for mobile AI app integrity detection tracked and maintained?

Evidence Required: - [ ] CSA Endpoint AI Safety Initiative contribution records: semantic event schemas for AI/HAI endpoint telemetry (DLP-decision events for AI prompt contexts, SaaS-AI admin-audit events, edge-device attestation events, mobile-AI integrity events); at least 2 schema contributions per year - [ ] OWASP MASVS contribution records: detection-pattern examples and verification criteria for mobile AI app security (local-model integrity verification, on-device AI data-boundary detection, secure enclave usage for AI operations); at least 1 MASVS contribution per annual cycle; in-progress or published - [ ] Sector ISAC submission records: anonymized endpoint-AI detection signatures (mobile-banking AI app integrity patterns for FS-ISAC, patient-facing chatbot abuse patterns for H-ISAC); legal-vet records per submission; at least one per month - [ ] Contribution maintenance evidence: schema versioning showing contributions updated when internal practice changes; not point-in-time submissions - [ ] Adoption tracking: records of external citations or integrations of contributed schemas; ISAC partners citing contributed signatures

Outcome Metrics: | Metric | Baseline | Current | Target | Met? | Notes | |--------|----------|---------|--------|------|-------| | Telemetry-standard contributions per year (CSA Endpoint AI Safety Initiative or equivalent) | ___ | ___ | ≥2 | ☐ | | | ISAC detection signatures contributed per year | ___ | ___ | ≥12 | ☐ | | | OWASP MASVS contributions for mobile AI app integrity (in-progress or published) | ___ | ___ | ≥1 | ☐ | | | Contributions with evidence of external adoption | ___ | ___ | ≥1 | ☐ | |

Metric Collection Guidance: - CSA contributions: Count schema or semantic-convention contributions to CSA Endpoint AI Safety Initiative or equivalent in the last 12 months. Source: contribution log. - ISAC signatures: Count anonymized endpoint-AI detection signatures submitted to sector ISACs in the last 12 months. Source: contribution log × ISAC submission receipts. - MASVS contributions: Count OWASP MASVS contribution items with status in-progress or published. Source: OWASP MASVS contribution tracking. - External adoption: Count external citations, integrations, or acknowledgments. Source: contribution tracking record.

Evidence Location: _____ Validation Date: ____ Notes: ______

Summary Scorecard

Question	Level	Activity
Q1: Per-Archetype Logging Baseline	L1	A
Q2: High-Signal Detection Set	L1	B
Q3: Deployer-Duty Evidence Trail	L1	C
Q4: Tier-Calibrated Logging Depth	L2	A
Q5: SIEM Integration and Cross-Archetype Correlation	L2	B
Q6: Anomaly Detection on Endpoint-AI Behavioral Patterns	L2	C
Q7: Detection-as-Code for Endpoints Domain	L3	A
Q8: Anomaly Detection at Scale	L3	B
Q9: Industry Contribution, Endpoints	L3	C

L1 Score (avg Q1–Q3): _ / 1.0 L2 Score (avg Q4–Q6): / 1.0 L3 Score (avg Q7–Q9): __ / 1.0 Overall ML-Endpoints Score (L1×0.5 + L2×0.3 + L3×0.2): _____ / 1.0

Assessor: _____ Assessment Date: ____ Next Review Date: ______

Document Version: HAIAMM v3.0, 2026-05-15, Verifhai Practice: Monitoring & Logging (ML) Domain: Endpoints Source of Truth: docs/practices/ML-Endpoints-OnePager.md Compliance Map: docs/HAIAMM-v3.0-Framing.md §10.2

Instructions:

Answer based on current practices, not plans
“Yes” requires documented evidence
Complete all Level 1 questions before Level 2
Partial implementation = “No”

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