This index exposes the material behind every decision guide: what we reviewed, when we retrieved it, the claim it supports and where the evidence stops.
173decision guides
765source records
757scoped claim notes
How to read this index
A citation is not a transferable result.
Reviewed identifies the part of the source used. Supports states the bounded proposition we draw from it. Retrieved records when time-sensitive material was checked.
Vendor documentation establishes vendor claims and product behavior as documented, not independent performance. Papers establish results only for their stated methods and tested scope. AccessAllGPT recommendations remain labeled analysis.
AccessAllGPT desk review of current OpenAI API data-control documentation, Amazon Bedrock data-protection documentation, the official GDPR text and the NIST Privacy Framework, retrieved 2026-08-11. Vendor statements, legal text and AccessAllGPT procurement guidance are labeled separately. This is not an original platform test, deletion audit, contract review, privacy impact assessment, security assessment, legal analysis or vendor comparison.
Reviewed: Data use; types of API data; abuse-monitoring retention; Zero Data Retention and Modified Abuse Monitoring eligibility and limitations; endpoint-level application-state retention; regional storage and processing controls; Enterprise Key Management
Supports: OpenAI states that API data is not used to train or improve its models unless a customer explicitly opts in, distinguishes abuse-monitoring logs from application state, documents default abuse-monitoring retention of up to 30 days, and describes feature-, project-, region- and eligibility-dependent controls. These are current vendor statements about the documented platform, not independent verification or a promise for every account and feature.
Reviewed: Shared-responsibility boundary; IAM, encryption and activity-logging recommendations; warning about sensitive data in tags and free-form name fields; model deployment accounts; linked encryption, PrivateLink and retention topics
Supports: AWS assigns customers responsibility for content controls and service configuration, warns that tags and free-form naming fields can enter billing or diagnostic logs, and states that model providers cannot access Bedrock deployment accounts, logs, prompts or completions. These are AWS service statements, not an audit of a customer configuration or a universal claim about cloud AI services.
Reviewed: Article 5 principles including purpose limitation, data minimisation and storage limitation; Article 28 processor requirements; Articles 44–49 transfers of personal data to third countries or international organisations
Supports: The official regulation text establishes principles and legal obligations relevant to processing, processor arrangements, retention and international transfers of personal data. It does not prescribe this article’s procurement workflow or determine whether a particular AI deployment is compliant.
National Institute of Standards and Technology · Retrieved
Reviewed: Framework purpose, voluntary risk-management positioning, Core and Profiles, implementation resources and relationship to enterprise risk management
Supports: NIST presents the Privacy Framework as a voluntary tool for identifying and managing privacy risk while building products and services. It provides risk-management structure, not certification, legal advice or approval of a provider.
AccessAllGPT desk review of current OpenAI evaluation and deprecation documentation, Anthropic evaluation guidance, HELM version 2 and the NIST Generative AI Profile, retrieved 2026-08-11. Vendor timelines, vendor guidance, research findings and AccessAllGPT procurement guidance are labeled separately. This is not an original platform test, migration exercise, grader study, cost benchmark, security assessment or vendor comparison.
Supports: OpenAI states that it announced deprecation of its Evals platform on June 3, 2026, that existing evals become read-only on October 31, 2026, and that the dashboard and API are scheduled to shut down on November 30, 2026. This is a vendor timeline for one product, not evidence that another platform is durable or that migration is lossless.
Reviewed: What evals are; evaluation process design; representative data and edge cases; evaluator types; human and model grading; continuous evaluation; Evals platform transition notice
Supports: OpenAI describes evals as structured tests for variable model behavior and recommends defining an objective, collecting representative and edge-case data, combining evaluator types and continuously evaluating. This is vendor-authored implementation guidance, not independent validation of a product or grader.
Anthropic Claude Platform Documentation · Retrieved
Reviewed: Success criteria; test-case construction; example metrics and measurement methods; automated, human and model-based grading; evaluation workflow and limitations
Supports: Anthropic recommends specific and measurable success criteria, multidimensional evaluation, representative test cases and a mix of grading methods. This is vendor guidance for evaluation practice, not independent evidence that Claude, a vendor console or model-based grading is accurate for a buyer’s workload.
Transactions on Machine Learning Research / arXiv · Retrieved
Reviewed: Abstract; scenario and metric taxonomy; multi-metric design; standardized evaluation scope; transparency artifacts; reported coverage and stated limitations
Supports: The HELM authors separate scenario coverage from metric coverage, evaluate multiple dimensions rather than accuracy alone, and publish raw prompts and completions for analysis. The reported study concerns its selected models, scenarios and historical period; it does not validate a procurement checklist or current commercial platform.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication metadata; abstract; voluntary cross-sector scope; relationship to AI RMF 1.0; design, development, use and evaluation framing
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 intended to help organizations incorporate trustworthiness considerations into the design, development, use and evaluation of generative AI systems. It does not certify an evaluation platform or prescribe this build-buy decision.
AccessAllGPT desk review of the Model Context Protocol tools specification version 2026-07-28, current OpenAI agent-safety guidance, OWASP LLM01:2025 and NIST AI 600-1, retrieved 2026-08-11. Normative and advisory protocol text, vendor guidance, security guidance and AccessAllGPT recommendations are labeled separately. This is not an original usability study, security test, incident study, legal analysis or measurement of approval effectiveness.
Reviewed: User Interaction Model, tool capabilities and tool invocation behavior
Supports: The specification says there SHOULD always be a human able to deny tool invocations and recommends interfaces that disclose exposed tools, indicate invocation and present confirmation prompts. It does not mandate a particular interaction model.
Reviewed: Prompt-injection and private-data risks, structured outputs, tool approvals, guardrails, trace graders and combined controls
Supports: OpenAI advises enabling approvals for MCP tool operations in Agent Builder and combining approvals with constrained data flow, guardrails and evaluation. This is vendor guidance for its products, not independent evidence that approval prevents unsafe outcomes.
Reviewed: Definition, impacts, prevention and mitigation strategies, and attack scenarios
Supports: OWASP says fool-proof prevention methods are unclear and recommends layered mitigations that include least privilege, independent authorization, segregation of untrusted content, monitoring and human approval for high-risk actions.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into the design, development, use and evaluation of generative-AI systems.
AccessAllGPT desk review of commit-pinned OpenTelemetry Generative AI conventions, the W3C Trace Context Recommendation, commit-pinned OWASP logging guidance and NIST AI 600-1, retrieved 2026-08-10. Standards, project guidance and AccessAllGPT recommendations are labeled separately. This is not an original platform test, incident study, cost benchmark, security assessment or vendor comparison.
Reviewed: Generative AI convention status and the linked spans and metrics specifications
Supports: OpenTelemetry labels its Generative AI semantic conventions Development and defines signal families for model and agent spans, events, exceptions and metrics. This establishes an evolving interoperability vocabulary, not production completeness or backend equivalence.
Reviewed: Span lifecycle, inference attributes, token usage and content-capture warnings
Supports: The development-status convention describes logical operation spans across retries, provider and model attributes, error type, token usage and opt-in input/output content fields; it warns that content fields are likely to contain sensitive or PII data.
Reviewed: Abstract, overview, traceparent, tracestate and privacy and security considerations
Supports: The W3C Recommendation standardizes HTTP headers and value formats for propagating request context across services. It enables distributed trace correlation; it does not define LLM quality, evaluation or prompt-retention policy.
Reviewed: Which events to log, event attributes, data to exclude, verification, protection and monitoring
Supports: OWASP recommends risk-proportionate logging requirements, exclusion or protection of sensitive fields, sanitization of event data, protection against unauthorized access or tampering and integration with monitoring and incident response.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
AccessAllGPT desk review of OWASP LLM01:2025, NIST AI 100-2 E2025, the indirect prompt-injection paper arXiv:2302.12173v2 and current OpenAI agent-safety guidance, retrieved 2026-08-09. Standards guidance, research findings, vendor guidance and AccessAllGPT recommendations are labeled separately. This is not an original penetration test, security assessment, control validation or prevalence study.
Reviewed: Definition, direct and indirect prompt injection, impacts, prevention and mitigation strategies, and attack scenarios
Supports: OWASP distinguishes direct from indirect prompt injection, ties impact to application context and model agency, says fool-proof prevention methods are unclear, and recommends layered mitigations including constrained behavior, output validation, least privilege, human approval and adversarial testing.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication metadata, planning notes, abstract, keywords and documentation links
Supports: NIST describes a taxonomy spanning ML methods, attack life-cycle stages, attacker goals, capabilities and knowledge, and identifies challenges and mitigation methods. The publication provides security terminology rather than certifying a specific prompt-injection control.
arXiv (authors from CISPA Helmholtz Center for Information Security and Saarland University) · Retrieved
Reviewed: Abstract, version history, stated threat model, demonstrated attack scope and limitations implied by the tested systems
Supports: The authors define indirect prompt injection through retrieved data and report demonstrations against named 2023-era real and synthetic LLM-integrated applications. This establishes practical examples in that historical scope, not a current prevalence rate or a universal exploit result.
Reviewed: Prompt injection and private-data risks, untrusted variables, structured outputs, tool approvals, input guardrails, trace graders, evaluations and combined controls
Supports: OpenAI advises keeping untrusted variables out of developer messages, constraining data flow with structured outputs, retaining tool approvals, applying input guardrails, and running trace graders and evaluations. This is vendor implementation guidance, not independent evidence that the controls eliminate prompt injection.
AccessAllGPT desk review of Anthropic engineering guidance, OpenAI’s agent-building guide and NIST AI 600-1, retrieved 2026-08-08. Vendor definitions and recommendations are labeled as such; the decision framework, gates and templates are AccessAllGPT guidance. This is not an original benchmark, agent trial, cost study, security assessment or vendor comparison.
Reviewed: Definitions; when and when not to use agents; frameworks; augmented LLMs; workflow patterns; autonomous agents; evaluation and production considerations
Supports: Anthropic distinguishes workflows with predefined code paths from agents whose process and tool use are directed dynamically by a model, recommends the simplest sufficient design, and describes latency, cost, control and evaluation trade-offs. These are vendor-authored engineering recommendations, not comparative trial results.
Reviewed: Pages 4–8, 13–17 and 24–32: agent definition, use-case criteria, design foundations, orchestration, guardrails and human intervention
Supports: OpenAI defines agents as systems in which an LLM manages workflow execution and dynamically selects tools, says deterministic solutions may suffice outside complex or ambiguous use cases, and recommends incremental orchestration, layered guardrails and human intervention. These are vendor-authored recommendations, not independent evidence of production performance.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation. It does not certify an agent or prescribe the AccessAllGPT decision framework.
AccessAllGPT desk review of NIST AI 600-1, current OpenAI API data-control documentation, vLLM stable engine configuration documentation and Kubernetes GPU-scheduling documentation, retrieved 2026-08-07. Vendor statements, implementation documentation and AccessAllGPT guidance are separated. This is not an original benchmark, cost study, security assessment or legal review.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Reviewed: Data use, abuse-monitoring retention, application state, retention controls and data residency
Supports: OpenAI states that API data is not used to train its models unless the customer opts in, describes default abuse-monitoring retention of up to 30 days, and documents feature- and eligibility-dependent retention controls. These are vendor statements about one managed service, not findings about every API or account.
Reviewed: Model, load, parallel, cache, device, scheduler and observability configuration groups
Supports: The serving engine exposes choices including model and tokenizer resolution, data type, quantization, model length, tensor and pipeline parallelism, GPU memory utilization, CPU offload and observability. This establishes configuration surface, not a universal performance or cost result.
Supports: Kubernetes documents vendor drivers and device plugins as prerequisites for GPU scheduling, GPU resources specified through limits, and node labels or affinity for selecting accelerator types. It describes a deployment mechanism, not a complete inference operating model.
AccessAllGPT desk review of the original RAG paper, NIST AI 600-1 and current OpenAI implementation guidance, retrieved 2026-08-06. Paper findings, vendor guidance and AccessAllGPT recommendations are labeled separately. This is not an original benchmark, cost study or product comparison.
arXiv (authors from Facebook AI Research, University College London and New York University) · Retrieved
Reviewed: Abstract, introduction, methods, experiments, results, discussion and limitations of the paper's experimental scope
Supports: The authors define and evaluate a specific RAG architecture that combines parametric sequence-to-sequence memory with retrieved non-parametric Wikipedia passages; this supports the architectural distinction, not a universal claim that retrieval improves every application.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Reviewed: LLM optimization context, prompt engineering, retrieval-augmented generation, fine-tuning and production accuracy sections
Supports: Vendor-authored guidance distinguishes context optimization for missing, stale or proprietary knowledge from model optimization for inconsistent format, style or behavior, and recommends an evaluate–hypothesize–change–evaluate cycle.
Reviewed: Overview, stated use cases, evaluation prerequisite, dataset construction and data-format guidance
Supports: Vendor-authored product guidance describes supervised fine-tuning with prompt-and-response examples for classification, translation, specific output formats and instruction-following behavior, and says to establish evaluations before investing in fine-tuning.
AccessAllGPT desk review of NIST AI 600-1, the HELM v2 paper, the original Chatbot Arena paper and OpenAI evaluation guidance, reverified 2026-08-06. Historical study findings, vendor guidance and AccessAllGPT recommendations are separated below. This is not an original benchmark, product test or vendor ranking.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Transactions on Machine Learning Research / arXiv · Retrieved
Reviewed: Abstract, methodology summary and reported evaluation scope
Supports: The authors define scenario coverage and multi-metric evaluation as distinct parts of model evaluation, and report standardized evaluation across accuracy, calibration, robustness, fairness, bias, toxicity and efficiency.
arXiv (authors from LMSYS Org and UC Berkeley) · Retrieved
Reviewed: Abstract, methodology description and stated validation claims
Supports: The authors describe Chatbot Arena as pairwise, crowdsourced human-preference evaluation and report agreement checks against expert raters; this establishes what the published ranking measures, not workload-specific production fitness.
Reviewed: What are evals, design your eval process, evaluator types and edge cases
Supports: Vendor guidance to define an evaluation objective, collect representative and edge-case data, combine evaluator types and continuously evaluate; used as implementation guidance rather than independent evidence of an OpenAI product claim.
AccessAllGPT synthesis of primary standards, security guidance, platform controls and benchmark methodology. This is not an original benchmark and does not score any vendor.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.