Enterprise AI
Zero data retention
Also known as: ZDR, zero-retention
Zero data retention is an arrangement in which an AI provider does not store your prompts or the model's outputs after the request has been answered. Data is held only transiently in memory to produce the response, then discarded rather than logged for abuse monitoring or model training. It is normally agreed contractually and enabled at the account or endpoint level for enterprise customers.
What it is
Under a zero retention arrangement, request and response payloads are not written to persistent storage by the provider. Standard API terms often retain data for a limited period for safety and debugging, so zero retention is a specific exception that has to be requested and approved. Providers usually scope it to particular endpoints or models, and some features that depend on stored state are unavailable.
Why it matters
Legal, security and procurement teams frequently block AI projects until retention questions are answered, so this setting is often what allows regulated work to proceed. It reduces the surface area for data exposure and simplifies answers about processing, transfer and deletion. For marketing teams it can be the difference between using a model on real customer data or only on sanitised samples.
How it works
Practitioners apply for zero retention with the provider, confirm which endpoints it covers, and record the arrangement in the data processing agreement and internal register. Because logs are not kept on the provider side, teams usually add their own redaction, monitoring and evaluation layer so they still have quality visibility. Architecture is reviewed so that features relying on stored conversation history or file stores are either avoided or handled in systems the business controls.
When it applies
It applies when prompts may contain personal data, client confidential material or regulated records, and when procurement or compliance requires that no third party stores the content of requests.
Examples
- A financial services marketing team gets zero retention approved before using a model to summarise complaint transcripts.
- An agency signs a client contract that requires zero retention on any AI system touching first-party customer lists.
- An in-house team routes sensitive briefs through a zero retention endpoint and everything else through the standard one.
How it is measured
- Proportion of AI workloads covered by a zero retention agreement
- Number of endpoints or models verified as in scope
- Compliance review cycle time for new AI use cases
- Volume of sensitive requests routed to non-retaining endpoints versus standard ones
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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