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Do not paste Singapore Government information into an AI chatbot until its classification, hosting location and provider terms have been checked. Singapore’s policy is risk-calibrated: highly sensitive applications and data are kept off the public Internet, while lower classifications may be processed by commercial services only in authorised environments with binding contractual and technical safeguards. Overseas-hosted APIs are limited to RESTRICTED and SENSITIVE NORMAL; Singapore-hosted APIs may handle up to CONFIDENTIAL and SENSITIVE HIGH only when the environment is authorised and cross-region processing has been ruled out.
What the Singapore Government’s approach means in practice
There is no blanket approval for every chatbot, and there is no blanket ban on every AI service. The permitted tool depends on the highest classification of information that will enter the system, where the model is hosted and inferred, what the provider does with prompts and outputs, and whether the deployment can act on its own.
In a 9 January 2024 parliamentary answer, Communications and Information Minister Josephine Teo stated that highly sensitive applications and data are not exposed to the Internet. Where sensitive-data use is necessary, models may instead be fine-tuned and deployed on Government servers. For less-sensitive workloads, commercial providers can be used only after the organisation has applied contractual, technical and governance safeguards proportionate to the data.
The 7 July 2026 Ministry of Digital Development and Information (MDDI) update expresses the principle directly: safeguards must be commensurate with the classification and sensitivity of the data involved. Treat the chatbot as part of an information system, not as a neutral text box.
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Classify the information before writing a prompt
Classification is the first control. An employee, contractor or system owner should know the highest permitted input classification before a prompt box, file-upload control or API is made available. The user and the data owner should verify the classification rather than relying on a chatbot’s default setting.
Use the highest classification in a mixed prompt
A prompt containing several individually harmless details can become more sensitive when the details are combined. A case number, a date, a location and a small extract from a record may identify a person or reveal an operational pattern. Classify the complete prompt and every attachment, not just the largest document.
Make the limit visible in the application
The Generative AI System Security Plan (SSP), last updated 24 March 2026, requires an internal application to state clearly the highest classification it permits. A warning should be visible before entry and again before an upload or API submission. If the application cannot state its permitted classification, do not use it for Government information.
Do not downgrade data merely to fit a tool
Removing a name does not automatically make a record safe. Redaction can leave unique facts, combinations of attributes or embedded metadata that identify a person, programme or location. If the resulting material still falls within a higher classification, it must use an environment authorised for that higher level.
Match the classification to the hosting location
Hosting location is a policy boundary, but a country label alone is not enough. Check where the service receives the request, where inference occurs, where prompts and outputs are cached, and whether support, telemetry or failover routes data to another region.
| API or deployment location | Maximum classification stated in the 2026 SSP | Conditions |
|---|---|---|
| Overseas-hosted GenAI API | RESTRICTED and SENSITIVE NORMAL | Use only within an authorised arrangement; verify provider processing, retention, training and cross-border paths. |
| Singapore-hosted GenAI API | CONFIDENTIAL and SENSITIVE HIGH | Use only through an authorised environment and after checking for cross-region inferencing or any overseas processing. |
| Self-hosted model | The highest classification authorised for the hosting environment | The model, surrounding services and operational environment must all be authorised for the highest data classification they process. |
| Highly sensitive application or data | Not exposed to the Internet | Use a Government-controlled deployment, such as a model fine-tuned and deployed on Government servers where the use case requires it. |
The limits are maximums, not automatic approvals. An overseas API that technically accepts a CONFIDENTIAL prompt is still outside the stated ceiling. A Singapore-hosted API is not acceptable if requests are routed to an overseas inference cluster or if support personnel can access retained content abroad.
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Check the provider’s legal and technical commitments
A marketing statement such as no training on your data is not enough. Obtain a legally binding commitment covering the actual service configuration. The commitment should state that input and output data are not logged, stored or retained and are not used to train models.
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The SSP says the commitment must cover every endpoint, parameter, output and model. Confirm that the same terms apply to:
- the chat interface and every API endpoint;
- file-upload, image, audio and retrieval features;
- moderation, safety, analytics and abuse-monitoring components;
- fine-tuning, evaluation and batch-processing jobs;
- fallback, failover and disaster-recovery systems;
- subprocessors and support or troubleshooting access; and
- new models or parameters enabled after the initial approval.
Ask for a written change-notification process. A provider can alter a model route, telemetry setting or retention default without changing the product name, so the approval should be tied to a specific configuration and reviewed after material changes.
Control prompt caching
Prompt caching is permitted only when the time-to-live is 24 hours or less. Record the configured TTL and confirm that cached prompts and outputs are deleted when that period ends. A shorter TTL is preferable for sensitive work. If the provider cannot state the cache duration or deletion behaviour, treat the service as unverified.
Separate provider assurances from your own controls
Non-retention does not prevent a user from copying a response into an uncontrolled document, email or ticket. Provider terms must be combined with access controls, auditability, approved storage and staff procedures. The organisation remains responsible for what it submits and where it stores the result.
Use an environment authorised for the highest classification
For a self-hosted model, the entire environment—not only the model weights—must be authorised for the highest classification of data processed. Include the API gateway, prompt store, vector database, logging pipeline, monitoring dashboard, backup system, administrator access and development copies in the assessment.
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Verify access and auditability
Before approval, document who can submit prompts, upload files, view conversations, export outputs, change model settings and administer the service. Use role-based access and retain an audit trail appropriate to the classification. Audit records should show the user, time, application, model or endpoint and action without unnecessarily copying the sensitive prompt into a lower-classification log.
Keep environments separated
Do not test a higher-classification prompt in a developer account or a public demonstration tenant. Keep development, test and production data paths separate, and ensure backups and support exports stay within an environment authorised for the same highest classification.
Treat file uploads as a separate leakage risk
Uploads can expose far more information than a typed question. A single spreadsheet may contain hidden columns, comments, formulas, revision history or thousands of records. Several individually permissible uploads can also create an aggregation risk.
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- Data-loss-prevention monitoring: inspect files and submissions for sensitive content before transmission.
- Restricted bulk uploads: disable bulk upload by default or limit it to an explicitly approved workflow.
- Per-file classification confirmation: require the user to confirm the classification of each file, not just the conversation.
- Large-file review: prompt a human review when a file is unusually large or contains an unexpected number of records.
Safer upload procedure
- Remove unnecessary rows, columns, comments, tracked changes and embedded metadata in an authorised preprocessing environment.
- Confirm the highest classification of the cleaned file with the data owner.
- Check that the destination API or deployment is permitted for that classification and that cross-region processing is excluded where required.
- Upload one file at a time unless a documented workflow authorises aggregation.
- Review the response for copied personal, operational or classified information before saving or sharing it.
- Delete temporary extracts and confirm that the chatbot’s retention and cache controls have operated as agreed.
A controlled workflow for using a chatbot
Use this sequence for every new use case, even when a service has been approved for another team.
- Define the task. State what the model must do and whether it needs the original data or can work from a synthetic example, a redacted extract or metadata only.
- Classify the inputs. The user and data owner identify the highest classification for the prompt, attachments and expected follow-up questions.
- Select an eligible deployment. Apply the overseas-versus-Singapore hosting ceilings and confirm that the environment itself is authorised.
- Verify processing paths. Check inference region, failover, support access, telemetry, caching and subprocessors. A Singapore endpoint is not sufficient if processing crosses the border.
- Verify the contract. Confirm legally binding no-log, no-retention and no-training terms for every endpoint, parameter, output and model, with a cache TTL of no more than 24 hours.
- Configure controls. Enforce identity and access restrictions, DLP, upload limits, audit logging and visible classification warnings.
- Minimise the submission. Send only the fields needed for the task. Prefer a synthetic or redacted example when it produces an equivalent result.
- Evaluate the output. Apply the approved accuracy, safety and quality tests before a human relies on the answer.
- Record accountability. Identify the human responsible for checking the result and for the final decision or communication.
- Reassess changes. Repeat the evaluation and security review after a model, prompt, dependency, endpoint, hosting route or provider term changes.
Evaluate outputs before anyone relies on them
Generative models can produce plausible but inaccurate or fabricated content. The SSP requires explicit acknowledgement of this risk and a human who remains accountable. A chatbot response is not an official record, legal interpretation or operational instruction merely because it is fluent.
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Define tests and metrics
Before production use, define representative test cases and measurable acceptance criteria for accuracy, safety and quality. Include normal requests, ambiguous wording, adversarial prompts, missing data, malformed files and attempts to elicit information outside the user’s authorisation.
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Re-run the tests after any model, system prompt, retrieval source, software dependency or security configuration update. A change that improves writing quality can also alter refusal behaviour, data exposure or factual accuracy. Keep the test results and approval decision with the system’s change record.
Require human review at the decision point
Assign a named role to verify facts against authoritative records, check that the output does not disclose restricted information and approve any external communication or consequential action. The reviewer should be able to see the source material needed to validate the answer without granting the chatbot broader access.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Agentic AI needs a fresh threat assessment
An ordinary generative chatbot returns text. An agentic system can call tools, retrieve records, send messages, modify files or take other actions autonomously. That difference changes the risk even when the underlying language model is the same.
The CSA’s Securing Agentic AI addendum, dated 17 June 2026, calls for a fresh threat assessment for agentic deployments. Reassess:
- which tools the agent can invoke and with what permissions;
- which data stores it can search or combine;
- whether a prompt can cause an unintended external action;
- how actions are approved, paused, reversed and audited;
- how credentials, tokens and secrets are isolated; and
- what happens when the model is manipulated by retrieved content or a malicious instruction.
Use least privilege, require confirmation before high-impact actions and provide a reliable stop or disable mechanism. Do not assume that a text-only chatbot approval automatically covers an agent with access to Government systems.
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Compare chatbot options using the right questions
When assessing a commercial service, a private deployment or an internal model, compare the complete control set rather than headline model quality.
| Question | Evidence to obtain |
|---|---|
| What classification is permitted? | The approved maximum classification for this specific application and user group. |
| Where is it hosted and where does inference occur? | Processing regions, failover routes, support access and any cross-region inferencing. |
| Are prompts and outputs retained or used for training? | A binding, scoped commitment covering every endpoint, parameter, output and model. |
| How long are prompts cached? | A configured TTL of 24 hours or less, with deletion behaviour documented. |
| Who can use and administer it? | Identity controls, role separation, approval workflow and audit records. |
| How are files protected? | DLP monitoring, per-file classification, bulk-upload restrictions and large-file review. |
| How are outputs tested? | Accuracy, safety and quality metrics, test cases, human accountability and retesting after updates. |
| Is it ordinary generative AI or agentic? | Tool permissions, data access, action approval, reversibility and a separate threat assessment for agents. |
If a provider will not supply evidence for one of these questions, record the gap and do not treat an unverified answer as approval.
What to do when the classification or processing path is unclear
Stop the submission. Do not test the service with real data to discover what it stores or where it routes requests. Ask the data owner, security team or Government information-security authority to resolve the classification and hosting questions.
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Keep the approval current
Security approval is tied to a system configuration and a use case, not just a product name. Review it when the model, prompt template, dependency, upload feature, endpoint, hosting region, provider contract or user population changes.
The relevant policy material is time-sensitive. The Generative AI SSP was last updated 24 March 2026; the broader System Security Plan framework was last updated 26 March 2026. The Institute of Digital Government’s Using AI responsibly guidance is dated 12 June 2026. CSA’s Securing Agentic AI addendum is dated 17 June 2026, while its Secure AI System Development guidelines date from 27 November 2023. MDDI issued its classified-and-unclassified-data update on 7 July 2026. Use the current versions adopted by your organisation when making an approval decision.
Bottom line
Singapore Government data should enter an AI chatbot only after classification, hosting, processing routes, retention terms, upload controls and human accountability have all been verified. Overseas-hosted APIs stop at RESTRICTED and SENSITIVE NORMAL; Singapore-hosted APIs can reach CONFIDENTIAL and SENSITIVE HIGH only in authorised environments with cross-region processing checked. Highly sensitive applications remain off the Internet. A no-training promise by itself is not approval: require binding no-log, no-retention and no-training commitments, keep prompt caching at 24 hours or less, protect uploads with DLP and per-file checks, test outputs after changes, and reassess any agent that can act through tools.
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