> ## Content Index > Fetch the complete content index at: https://www.aigl.blog/llms.txt > Use this file to discover other available public pages before exploring further. # AI Organizational Responsibilities: AI Tools and Applications - URL: https://www.aigl.blog/ai-organizational-responsibilities-ai-tools-and-applications/ - Published: 2026-09-19T09:13:24.000Z - Updated: 2026-09-19T09:13:24.000Z - Description: Third white paper in the Cloud Security Alliance AI Organizational Responsibilities series, examining the practical implementation of AI tools and applications: LLM and GenAI security, third-party and supply chain management, and AI operations. - Author: Jakub Szarmach - Tags: Whitepaper, AI Security, AI Governance, Supply Chain & Third-Party Risk, Risk Management, Compliance & Audit, #aigl-library ## ⚡ Quick Summary Published by the Cloud Security Alliance, this white paper is the third instalment in its AI Organizational Responsibilities series, following papers on core security responsibilities and on governance, risk management, compliance and cultural aspects. This instalment addresses the practical implementation of AI within organizations: the tools, applications and supply chains used to deploy AI-driven systems. Every responsibility is analysed through six repeated dimensions: Evaluation Criteria, a RACI Model, a High-Level Implementation Strategy, Continuous Monitoring and Reporting, Access Control Mapping, and Adherence to AI Standards and Best Practices. The content is organised in three parts: LLM or GenAI App/Tools Security (secure development, prompt injection defence, output evaluation and guardrails, operational and performance qualification, access control and privacy responsibility); Third Party/Supply Chain Management (vendor assessment, procurement, acceptable certifications, contractual obligations, due diligence, dependency monitoring, data usage agreements, SBOM and SLSA, shared responsibilities); and Additional AI Implementation and Operations Considerations covering employee use of GenAI tools, operationalizing GenAI for security operations centres, and the differences between AI and traditional IT responsibilities. The paper closes with a conclusion, six key takeaways and a future outlook. ## 🧩 What’s Covered - **Introduction, assumptions and audience**: positions the paper as the third in the series, sets out the six dimensions applied throughout (evaluation criteria, RACI, implementation strategy, monitoring and reporting, access control, standards adherence), states an industry-neutral stance, and lists audiences from CISOs and developers to policymakers and investors. - **Responsibility role definitions**: tables of roles across management and strategy, governance and compliance, technical and security, and operations and development, with the caveat that organizations may fold these functions into existing roles. - **LLM or GenAI app/tools security**: seven subsections covering secure LLM application development, prompt injection defence, output evaluation and guardrails, operational qualification, performance qualification, access control and privacy responsibility. - **Third party/supply chain management**: ten subsections on LLM vendor assessments, procurement, acceptable certifications and third-party reports, contractual obligations, screening and due diligence, dependency monitoring, data usage agreements, SBOMs in CycloneDX and SPDX formats, SLSA levels and shared responsibilities. - **Additional implementation and operations considerations**: employee use of GenAI tools (policy, training, vetting, approved-tool lists), operationalizing GenAI for security operations centres including Red-Amber-Green and simulation-based impact scales, and a feature-by-feature comparison of non-deterministic AI systems with deterministic traditional IT. - **Normative references and closing material**: a reference list including the CSA Cloud Controls Matrix, OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF and SSDF, GDPR and the EU AI Act proposal, followed by a conclusion, six key takeaways and a future outlook. ## 💡 Why it matters? For teams that must allocate AI responsibilities, the paper supplies a repeatable structure: each topic carries evaluation criteria, a RACI mapping, implementation steps and control expectations, so it can be used to build or test an internal AI responsibility matrix. It links each topic to the external instruments the document itself cites, including NIST AI RMF, ISO/IEC 23894:2023, the OWASP Top 10 for LLM applications, GDPR, CCPA/CPRA, SBOM formats and SLSA. It also sets out how non-deterministic AI systems change monitoring, testing, incident response and version management relative to traditional IT. ## ❓ What’s Missing Recommendations stay high-level, mostly bullet lists of actions without worked examples, templates, threshold values or sizing guidance. Several subsections close with a short adherence list that names standards without explaining how conformance is demonstrated. Regulatory coverage consists of citations to data protection and AI instruments, with the EU AI Act referred to as a proposal and no legal analysis of any specific regime. The role tables are explicitly illustrative and read as reference material rather than an operating model, and some subsections, such as those on certifications and data usage agreements, offer only a few criteria. The acronym AUT is expanded inconsistently, once as User Activity Monitoring Tools and once as Acceptable Use of Technology. ## 👥 Best For Best for CISOs, AI governance leads and risk, compliance or vendor-management teams that need a checklist-style map of AI responsibilities across security, supply chain and operations, and for staff drafting RACI matrices, vendor assessment questionnaires, GenAI acceptable-use policies or SOC integration plans. It also suits programme leads aligning internal controls with the named external standards and frameworks. ## 📄 Source Details *AI Organizational Responsibilities: AI Tools and Applications*, a Cloud Security Alliance white paper, 71 pages, English, carrying the notice "© Copyright 2025, Cloud Security Alliance"; no edition or reference number is printed. Lead authors are Nick Hamilton, Ken Huang, Michael Roza and Maria (MJ) Schwenger; contributors and reviewers are listed separately. The full text of all 71 pages was available, but diagrams such as the Lifecycle of AI Organizational Responsibilities appear only as captions. The working group page printed on page 2 is [https://cloudsecurityalliance.org/research/working-groups/ai-organizational-responsibilities](https://cloudsecurityalliance.org/research/working-groups/ai-organizational-responsibilities?ref=aigl.blog).