Feature
As generative AI becomes common in enterprise workflows, a question emerges for compliance and records managers: Are AI prompts and outputs considered business records, and are they being preserved?
Most organizations are only beginning to confront this gap. While AI is widely used, few vendors provide native tools to capture, retain and audit these interactions, leaving businesses at risk when audits or legal holds occur.
Table of Contents
- Records Management Adjusts in Response to AI Retention Needs
- 10 Different Approaches to AI Prompt and Output Retention
- Long-Standing Enterprise Vendors
- IBM watsonx.governance
- Iron Mountain InSight (DXP)
- Microsoft Purview
- OpenText
- Veritas (part of Cohesity)
- Specialist Vendors
- Arize AI
- Langfuse
- PromptLayer
- Requesty
- Vellum
Records Management Adjusts in Response to AI Retention Needs
A few vendors are starting to fill the gap. Microsoft Purview treats Copilot prompts and responses as discoverable, with retention and legal hold capabilities. Relativity converts ChatGPT and Gemini threads into reviewable records, and Smarsh, Theta Lake and Proofpoint layer storage with policy validation across channels.
Other enterprise content management (ECM) vendors, such as OpenText (Aviator), iManage, Hyland and M-Files, integrate AI assistance but have not clarified whether prompts or outputs are preserved, leaving teams to rely on workarounds such as transcript exports or API logging.
But things are changing. Andrew Gamino-Cheong, CTO of Trustible, highlights the prevalence of "shadow AI" and the difficulty of unified logging, while Monika Malik, AI governance engineer, notes that regulators already consider prompts and outputs as records. Businesses must combine audit logs, LLM tracing and retention hooks to ensure compliance before regulations force the issue.
The definition of what constitutes a business record in the AI context remains murky, with organizations grappling over whether every prompt-response pair qualifies or only those tied to specific business decisions. This ambiguity complicates retention strategies, as teams struggle to determine which interactions need to be preserved under existing records management policies.
Beyond definitional challenges, organizations face hurdles in data privacy, storage costs and technical implementation when preserving AI interactions. Storing every prompt and output quickly becomes expensive, while ensuring sensitive information in prompts doesn't violate privacy regulations makes an already challenging technical implementation more complex.
10 Different Approaches to AI Prompt and Output Retention
So what is available now? On one side are the enterprise incumbents — Microsoft, OpenText, IBM, Iron Mountain, Veritas — which already sit at the center of compliance, records and governance workflows. If regulators decide prompts and outputs are business records, these are the vendors most likely to extend their platforms to capture them.
On the other side are specialist startups such as PromptLayer, Langfuse and Vellum, which treat prompts as first-class artifacts, offering logging, versioning and trace capture. They represent a bottom-up push from AI engineering teams that need simple, API-friendly ways to manage prompt history.
In-between are vendors such as Arize AI, which bring monitoring and evaluation records into scope, and Requesty, which captures prompts at the infrastructure layer by acting as a gateway across providers.
Together, these companies illustrate the current state of play: Only a few tools preserve prompts and completions, while most incumbents are just beginning to extend their compliance stacks. This mix highlights both the gap enterprises face today and the direction the market is moving.
Long-Standing Enterprise Vendors
Chances are that an organization with the need for records management in the AI context will already be using one of these vendors. Here’s what they offer.
IBM watsonx.governance
What it offers: watsonx for building and hosting GenAI plus watsonx.governance for model governance, monitoring, auditing and explainability.
What it does: Manage model experiments, track model lineage, monitor model behavior and provide audit-ready evidence about AI decisions and deployments.
Standout: Enterprise-grade governance focused on explainability and regulatory needs, with tooling to centralize model artifacts and audit trails.
Why noteworthy: Explicitly marketed watsonx.governance as one platform for responsible, auditable AI.
Prompt Preservation for Recordkeeping: Watsonx.governance lets organizations capture model inputs (prompts) and outputs as part of its lineage tracking. Prompts become part of the experiment and inference record, linked to model versions and outcomes. This creates an auditable trail of what was asked, how the model responded and under what configuration.
Iron Mountain InSight (DXP)
What it offers: Traditional records management platform that adds intelligent document processing (IDP) and secure generative-AI chat for documents.
What it does: Capture and digitize physical and electronic records, provide audit-ready storage and a secured environment for querying documents with embedded GenAI that keeps AI queries inside a controlled environment.
Standout: Expertise in legal/regulatory retention, chain-of-custody and secure physical and digital hybrid records — useful where prompts or AI outputs must be preserved as records.
Why noteworthy: Positioning itself to keep AI interactions and document queries "audit-ready" inside an enterprise records infrastructure.
Prompt Preservation for Recordkeeping: InSight DXP's secure chat interface with generative AI logs queries made against digitized records. Those prompts and AI responses are retained in Iron Mountain's repository as immutable records, subject to existing classification and retention rules. This approach brings AI interaction histories under the same governance umbrella as traditional documents.
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Microsoft Purview
What it offers: Data governance, compliance and risk tools extending to GenAI/LLM use (policy controls, data classification, audit trails, protections for data used with AI).
What it does: Integrate AI usage policies into enterprise governance (such as, prevent sensitive files being uploaded to LLMs), capture audit logs and data lineage across Microsoft 365 and Azure data services.
Standout: Tight integration with Microsoft 365/Azure ecosystems; good for organizations already invested in that stack.
Why noteworthy: Microsoft has pushed product updates specifically calling out GenAI governance and Purview guidance for AI risk mitigation.
Prompt Preservation for Recordkeeping: Purview doesn't yet natively store AI prompts as "records" objects, but it can log user interactions with Microsoft Copilot and Azure OpenAI in audit trails. These logs can be classified and retained under Purview's retention policies, effectively capturing who prompted what and when. Integration with Microsoft 365 ensures prompts are tied back to user identity and compliance systems.
OpenText
What it offers: Enterprise content and records management plus GenAI features (Content Aviator, capture, IDP integrated with generative AI)
What it does: Capture documents, extract metadata, embed generative features while preserving content governance and retention policies; useful for making AI outputs discoverable and auditable in an ECM.
Standout: Deep content-centric platform (capture, governance, discovery) designed for regulated industries with existing records workflows.
Why noteworthy: OpenText added GenAI / Content Aviator capabilities into its content platform, aligning LLM features with existing information governance.
Prompt Preservation for Recordkeeping: OpenText's GenAI-enabled content services capture prompts and completions used in its Content Aviator workflows. Because Aviator is built-in to the content platform, prompts and their outputs are stored as governed content items with metadata and retention schedules, so they are retrievable in audits or litigation.
Veritas (part of Cohesity)
What it offers: Information governance, data classification and AI-driven compliance capabilities that help discover, classify and protect content across clouds.
What it does: Automated classification and policy enforcement to find where prompts, RAG (retrieval-augmented generation) sources or LLM outputs are stored; helps enforce retention and e-discovery.
Standout: Focus on multi-cloud data resilience as well as AI-driven classification to reduce data sprawl and help meet compliance and/or audit requests.
Why noteworthy: Product messaging and releases tying AI features to improved observability / governance of enterprise content.
Prompt Preservation for Recordkeeping: Veritas doesn't provide native prompt capture, but its data governance tools integrate with AI usage logs from Microsoft, Google or API gateways to ingest and classify prompts and outputs. Once ingested, prompts are subject to enterprise-wide retention and discovery policies, so they can be recalled during audits or legal proceedings.
Specialist Vendors
For organizations not in the market for traditional records management features, they may find some of the start-ups that provide LLM prompt logging, monitoring and observability better suit their requirements. Here are some of the options.
Arize AI
What it offers: Enterprise LLM observability, evaluation and monitoring (metrics, drift detection, evaluation suites and alerting).
What it does: Continuously evaluate LLM outputs, detect quality regressions or drift and retain evaluation artifacts, which supports compliance and demonstrable testing/validation history.
Standout: Built for ML/AI teams that need production monitoring and evaluation; substantial funding and enterprise traction.
Why noteworthy: Funding and is positioning LLM observability as a core function for safe deployment, which is useful to show "we tested and monitored" in audits.
Prompt Preservation for Recordkeeping: While Arize is evaluation-focused, it records the prompts and completions used in evaluation runs, along with scoring metrics. Archiving these artifacts helps organizations demonstrate not only what prompts were used in testing but also how outputs were judged. This is important for showing due diligence in regulated environments.
Langfuse
What it offers: Tracing, observability and metrics for LLM apps: capture inputs, outputs, multi-step traces, tool calls, latencies, cost and per-step metrics; both open-source and hosted options.
What it does: Create end-to-end traces for each user-LLM interaction (including intermediate steps) for reproducible records and forensic analysis of multi-turn/agent behavior.
Standout: Tracing focus (not just prompt logging)captures structured traces of agents and complex workflows, which is useful where you must explain how an output was produced.
Why noteworthy: Fills the gap between raw prompt logs and full application-level traces, which is important for recordkeeping and incident investigation in LLM apps.
Prompt Preservation for Recordkeeping: Langfuse captures and stores full traces, including every prompt, intermediate tool call and LLM response. Its structured trace format means prompts are preserved with context (user, session, model version). This creates reproducible, auditable records of multi-step AI interactions that are exported or retained in compliance systems.
PromptLayer
What it offers: Prompt logging, versioning, a prompt CMS, side-by-side comparison of prompts/completions, tagging and basic analytics/evaluations
What it does: Persistently logs prompts and completions with a simple, structured record of each LLM interaction so organizations retain, search and audit prompt history and outputs
Standout: Lightweight, easy to integrate for teams calling OpenAI APIs; focused specifically on prompt lifecycle (versioning, collaboration, audit trail)
Why noteworthy: Widely used in LLM engineering circles as a pragmatic way to capture prompt→response pairs and manage prompt versions — a direct fix to the "where are my prompts?" problem
Prompt Preservation for Recordkeeping: PromptLayer's core function is to persist every prompt and completion, versioned and tagged, in a structured database. This makes prompts first-class records, which lets you search, export or snapshot them. Organizations can lock prompt histories for compliance purposes, satisfying recordkeeping obligations directly.
Requesty
What it offers: An API gateway/infrastructure layer between organizations and model providers that centralizes governance, observability, cost controls, prompt caching and routing
What it does: Centralize and log AI requests from across an organization, enforce rules such as blocking PDFs from being uploaded to external LLMs, provide unified audit logs and cost/usage records
Standout: Infrastructure-level control for multi-provider AI stacks like Cloudflare for LLMs, which helps organizations consolidate logs and policies across many AI endpoints
Why noteworthy: Emerging as a practical way to centralize prompt traffic and governance, which is helpful el where teams use many models/providers but need one audit trail and policy layer
Prompt Preservation for Recordkeeping: Because Requesty acts as a gateway, it logs every API request (prompt) and corresponding response before they are sent to model providers. These logs are centralized, exportable and can be retained under policy, making it one of the cleanest approaches for creating a unified, organization-wide prompt archive.
Vellum
What it offers: A prompt sandbox and prompt management product for systematic prompt iteration, comparison across models and evaluation in a team setting.
What it can do: Store prompt templates, results, perform comparisons and build a searchable record of prompt experiments and outputs (good for reproducibility and retention).
Standout: UX-focused prompt engineering product (side-by-side model comparisons, templating), geared at production teams refining prompts safely.
Why noteworthy: Makes prompts first-class artifacts to store, version and retain.
Prompt Preservation for Recordkeeping: Vellum stores prompts, templates and results as artifacts within its workspace. Prompts are version-controlled, annotated and linked to outcomes, then exported as structured records. This makes Vellum especially suited to preserving the history of prompt engineering and deployment in regulated contexts.
Editor's Note: Read more about trends in the information management space:
- Enterprise Content Management Gets Disrupted, Again — Keeping up with all the name changes ECM has gone through over the years is hard. But the latest – intelligent content services – might just have holding power.
- Document Management Evolves, With Blockchain, Insights and Automation — Document management is no longer just storage, it’s becoming a core enabler of intelligent, secure workflows.
- Why OpenText Fired Its CEO After 13 Years: The End of the Shopping Spree — Mark Barrenechea's 13-year run as OpenText's CEO came to an abrupt end on August 10. What that means for the ECM giant.