Industry Solutions

What should an enterprise AI security checklist for government cover?

A government AI security checklist covers data classification, sovereignty and residency, procurement and vendor assurance, identity and access, auditability, and records retention. Public-sector deployments often require in-country processing and full traceability of decisions. Keep sensitive workloads on sovereign, on-prem or air-gapped deployments, log every interaction, and preserve records according to statutory retention rules.

Key facts

DeploymentSovereign, VPC, on-prem and air-gapped options for in-country processing
Data residencyRegion selection and sovereign deployment choices
Access controlScoped API keys with rotation; enterprise SSO and RBAC options
AuditabilityRequest, model and response logging for public accountability
Data groundingEmbeddings and RAG are live for legislation, policy and service content
Models30+ models behind one OpenAI-compatible API
Pricing modelFlat monthly self-serve plans; no per-token billing on self-serve
Endpoint roadmapAudio, images, moderation, batch and fine-tuning are coming soon

TL;DR

  • Classify data first; sovereignty requirements differ by class and agency.
  • Prefer sovereign, on-prem or air-gapped deployment for sensitive workloads.
  • Scope access by role and keep named accountability for every workflow.
  • Log interactions to support public accountability and audit requests.
  • Apply statutory retention and disclosure rules to prompts, outputs and logs.

How it works, step by step

  1. Classify data by sensitivity and map the residency and handling requirements for each class.
  2. Decide the deployment per class: region-selected cloud, sovereign, on-prem or air-gapped.
  3. Complete vendor assurance: architecture, data flow, security documentation and SLA review.
  4. Scope keys and console access by role, with rotation and a central inventory.
  5. Ground answers in current legislation and policy, requiring citations per response.
  6. Define retention and disclosure rules for prompts, outputs and logs, and apply them technically.
  7. Publish an internal accountability map: who owns each workflow and who reviews outputs.
1Classify data bysensitivity and mapthe residency and2Decide thedeployment perclass:3Complete vendorassurance:architecture, data4Scope keys andconsole access byrole, with rotation5Ground answers incurrent legislationand policy,6Define retentionand disclosurerules for prompts,

Try it yourself

Open the sovereign AI readiness score →

Sovereignty and procurement

Public-sector buyers need evidence, not assurances. Prepare the package once: an architecture diagram, a data-flow map for each service, security and retention documentation, and the service agreement terms. Plugsky supports region selection plus sovereign, on-prem and air-gapped deployment, and the OpenAI-compatible API means a proof-of-concept built on public content transfers to the production environment without code changes.

See the procurement checklist for the questions to formalize early.

Identity, access and insider risk

Government systems carry insider and accountability risks as much as external ones. Issue keys per application and environment, keep them in a secrets manager, and separate build, approval and operate roles so no single person can change a production workflow unchallenged. Enterprise SSO and RBAC options align console permissions with personnel records and revocation processes. Quarterly access reviews keep the map current as agencies reorganize.

Auditability and records retention

Every automated interaction that informs a public decision should be reconstructable: request ID, model and version, retrieved sources, output and the responsible officer. Apply statutory retention schedules to those records, and make deletion technically enforced rather than aspirational. Where disclosure rules apply, the log structure should make redaction practical without losing the audit trail.

Public communication and accountability

Citizens deserve to know when AI is involved in a service and what they can do about an error. Publish a plain-language description per service: what the system does, what data it uses, who reviews outputs, and how to appeal. Internally, name an accountable owner for each workflow and review a sample of interactions regularly so quality and fairness issues surface before they become public.

Honest comparison

Control areaPlugsky capabilityCommon gapOwner
SovereigntySovereign, VPC, on-prem and air-gapped deployment optionsData processed outside the jurisdictionProgram office
Vendor assuranceArchitecture, data-flow and SLA documentationNo evidence pack preparedProcurement
AccessScoped keys, rotation, enterprise SSO and RBAC optionsShared credentials across agenciesSecurity
AuditabilityRequest, source and officer-decision loggingDecisions not traceableAudit and records
RetentionConfigurable logging under statutory schedulesDeletion not enforcedRecords management
TransparencyCitations and structured output for reviewAI involvement undisclosedCommunications

Frequently asked questions

Can processing stay inside the country?

Yes for supported regions, and by construction with sovereign, on-prem or air-gapped deployment. Confirm the specific region and data flow in writing during procurement.

What evidence do we need for procurement?

Architecture and data-flow diagrams, security and retention documentation, SLA terms and an access-control model. Prepare it once and reuse it across services.

How do we make decisions auditable?

Log request ID, model and version, retrieved sources, output and the responsible officer. Retain per statute and restrict access to authorized staff.

Is Arabic supported for citizen services?

Multilingual chat and embedding models are in the 30+ model catalogue. Evaluate on real service questions and local dialects before enabling a channel.

What about appeals when the AI is wrong?

Design an appeal path per service, name an accountable owner, and keep the interaction log available so the case can be reviewed against the same evidence.

Which endpoints are live today?

Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, assistants, responses and fine-tuning are coming soon.

Where should a pilot start?

Use public legislation and service content on the free plan, validate citations and logging, then move sensitive services into a sovereign or private deployment.