No-log AI is an AI service that does not keep server-side records of user conversations after a request is processed. The prompt still passes through the model to generate a response, but the provider does not persist the chat history on its infrastructure unless another feature, such as local sync, stores it elsewhere.
Expanded Definition
No-log AI describes an AI service that processes a prompt without persisting server-side conversation records after the request completes. It is narrower than “privacy-focused AI” because the claim is about storage behavior, not model training, encryption, or user anonymity. In practice, the term is often used alongside retention controls, ephemeral session handling, and data minimisation, but definitions vary across vendors. A provider may still process content in memory, route it through observability pipelines, or store metadata for abuse prevention, billing, or reliability. That means “no-log” is not the same as “no data ever touched the provider.”
For governance teams, the key question is where retention ends and whether any copies exist in backups, analytics systems, or linked products. This distinction aligns closely with broader guidance in the NIST Cybersecurity Framework 2.0, especially where data handling and access control intersect. The most common misapplication is assuming a no-log claim covers all telemetry, which occurs when organisations overlook debug traces, admin consoles, or connected storage that preserves prompts elsewhere.
Examples and Use Cases
Implementing no-log AI rigorously often introduces a traceability tradeoff, requiring organisations to weigh privacy and reduced data exposure against incident investigation, abuse detection, and compliance evidence.
- A legal team uses a no-log AI assistant for drafting sensitive documents, while keeping the source material outside the service to reduce exposure of privileged content.
- A healthcare organisation pilots a no-log AI workflow for intake summaries, but separately validates whether any API gateway logs, browser caches, or sync tools retain patient identifiers.
- A software team evaluates an AI coding assistant after a secret-leak incident, pairing no-log claims with repository scanning guidance from The State of Secrets in AppSec and the retention principles reflected in the NIST Cybersecurity Framework 2.0.
- An incident response group chooses ephemeral AI sessions for triage notes, because the operational need is fast summarisation without creating a persistent record inside the vendor environment.
- A procurement team compares vendors after reading about DeepSeek breach, using that case to test whether “no-log” language is backed by actual data-flow design.
Why It Matters in NHI Security
No-log AI matters because NHI risk often emerges when high-value prompts contain credentials, internal architecture, customer data, or incident details. If those inputs are retained, they become searchable artifacts that can be exposed through misconfiguration, subpoena, support access, compromise, or unintended sync features. NHIMG research shows that only 44% of developers follow security best practices for secrets management, which helps explain why prompt hygiene and AI retention controls must be treated as one problem rather than separate ones. That concern becomes sharper when AI systems may learn or reproduce sensitive patterns from codebases, as highlighted in The State of Secrets in AppSec. For NHI programs, the question is not only whether a tool stores chats, but whether it ever becomes a secondary repository for secrets, tokens, or operational intelligence. A “no-log” claim can support lower exposure, but it does not replace classification, secret redaction, or least-privilege access to the surrounding workflow. Organisations typically encounter the operational cost only after a secret is pasted into an AI chat and later discovered in another system, at which point no-log becomes operationally unavoidable to verify and enforce.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST Zero Trust (SP 800-207) and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | No-log claims intersect with secret handling and retention exposure in NHI workflows. |
| NIST CSF 2.0 | PR.DS | Defines data security protections relevant to prompt retention and storage minimisation. |
| NIST Zero Trust (SP 800-207) | Zero trust supports limiting implicit trust in provider-side handling and downstream copies. | |
| NIST AI RMF | Risk management requires assessing data retention impacts on privacy and security. | |
| OWASP Agentic AI Top 10 | Agentic systems can retain or reveal sensitive context through tool use and logs. |
Assume prompt data can persist elsewhere and enforce explicit verification of every storage path.
