ODA3-2026-07-CHT-SEC-009
AI Security: Essential Evidence Collection Strategies
Most organizations discover their AI evidence gaps mid-incident, mid-audit, or mid-assessment—when it is too late to recover what was never logged.
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Insights
Published research, incident analysis, regulatory context and practitioner guidance. Each article should be read with its stated sources, scope and limits.
Open to read · Publication counts below reflect the published records currently available on this site.
Editorial and evidence standard
Check the publication ID and date, the source basis, the claim being made and its limits. A published article does not by itself establish an assessment, certification or finding about a particular system. Corrections should remain visible and point to the current authoritative version.
PUBLICATIONS
Research intelligence and analysis.
About this publication catalog
The catalog includes the published WordPress records available on this site. Search matches titles, article text, publication IDs, tags and recorded evidence-basis fields. Category and year narrow the results; sort changes their order. A match is a retrieval result, not an endorsement or evidence-tier judgment.
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90 published recordsAll (90)Regulatory Intelligence (26)Research Report (16)Practitioner Guide (16)Control Framework Update (10)Incident Analysis (8)Defensive Publication (4)AI Assurance (1)Institute Announcement (1)Standards Development (1)Applied Research (1)Viewpoint (1)Thought Leadership (1)Framework Release (1)Operational Assurance Analysis (1)ODA3 Insights (1)News / Announcement (1)
90 publications found
ODA3-2026-07-CHT-SEC-008
AI Security Design Principles: Cheat Sheet
Nine design principles for building enforceable security into AI systems before deployment.
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ODA3-2026-07-CHT-SEC-007
AI Security Control Validation Guide: Cheat Sheet
A practical method for proving that documented AI security controls operate as intended.
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ODA3-2026-07-CHT-SEC-006
AI Security Assurance Framework: A Practitioner Cheat Sheet
A structured way to connect governance claims, operating controls, evidence, and bounded conclusions.
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ODA3-2026-07-CHT-SEC-005
AI Security Architecture Patterns: A Practitioner Cheat Sheet
Placing controls at the right layer across model, retrieval, agent, tool, and enterprise boundaries.
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ODA3-2026-07-CHT-SEC-003
Red Teaming Is a Process, Not a Party Trick: AI Red Teaming Methodology
A structured, authorized and evidence-producing method for adversarial testing of AI systems.
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ODA3-2026-07-CHT-SEC-004
AI Risk vs Security Risk Matrix Cheat Sheet
A routing matrix for distinguishing harmful system behaviour from adversarial compromise and overlap.
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ODA3-2026-07-CHT-SEC-002
AI Monitoring Architecture Cheat Sheet
Monitoring model behaviour, retrieval, agent actions, tool use, and infrastructure as one evidence chain.
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ODA3-2026-07-CHT-SEC-001
Prompt Injection Mitigation Controls: Why Layered AI Security Matters More Than Prompt Engineering
A layered control model for reducing prompt-injection impact across AI applications, RAG and agents.
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ODA3-2026-07-TCR-SEC-004 / ODA3-2026-07-EXB-SEC-005
Understanding AI Incidents: Q2 2026 Findings
Q2 evidence indicates that material AI security impact is shifting from model outputs toward authorized actions.
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ODA3-2026-06-DP-009
Dominant-Harm-Anchored Severity Scoring Engine for AI Incident Classification with Diminishing-Contribution Context Modeling and Calibration-Aware Regulatory Trigger Confidence Bands
ODA3 defensive publication establishing prior art for Dominant-Harm-Anchored Severity Scoring Engine for AI Incident Classification with.
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ODA3-2026-06-DP-010
Cryptographic Hash-Based Incident Deduplication with Architecturally-Separated Acute and Chronic Harm Classification for AI Security Incident Records
ODA3 defensive publication establishing prior art for Cryptographic Hash-Based Incident Deduplication with Architecturally-Separated Acute and Chronic.
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