Gen Ai Security

gen-ai-security: GA controls for SG government GenAI features

You are building or auditing a Generative AI feature against the ICT&SS Policy Reform Gen-AI SSP overlay: the 8 GA controls (GA-1..GA-8) plus DP-8 — nine controls in total. The family's scope is securing the use of GenAI models and applications: where the model runs, what data may reach it, the provenance of self-hosted weights, input safeguards, output evaluation, and user awareness. Most of these are decisions you bake in on day one, and exactly what an audit or VAPT of a GenAI service walks through.

The single decision everything hangs on: the maximum data classification the feature handles determines which model/provider you may call. Get that boundary right first; the rest follows.

What this family is not about

The GA controls are about data-classification governance, provider agreements, weight provenance, input safeguards, output evaluation, and user awareness — not primarily about prompt-engineering defenses. Two things people wrongly expect here:

• Prompt injection / jailbreak defense has no separately named GA control. It is tested

under GA-7 (safety) and fixed as Application Security (secure-coding-as). Cover it — but don't cite a non-existent "GA prompt-injection control."

• The provider API key is an application secret (AS-8, secure-coding-as), not GA-3.

GA-3 governs the provider's contract, not how you store the key.

Source and currency

Control text in this skill and references/ga-controls.md is embedded from info.standards.tech.gov.sg as of 2026-07-22; the pages were last updated 24 March 2026. The standards iterate actively. For any compliance-critical decision, verify against the live pages:

• GA control catalog: https://info.standards.tech.gov.sg/control-catalog/cybersecurity/ga/
• Gen-AI SSP overlay: https://info.standards.tech.gov.sg/ssp/gen-ai/

Reference files

• references/ga-controls.md — full text of all 8 GA controls + DP-8 (statement,

recommendations, risk, the two GA-5 parameters), data-classification background, and cross-family notes. Read it when you need exact wording, e.g. for an audit response or SSP documentation.

• references/implementation-recipes.md — concrete, framework-neutral code and config per

control: provider-routing by classification, the GA-3 agreement checklist, safetensors / approved-loader loading, file-upload safeguards, DP-8 input disclosure, the GA-7 eval harness, and the GA-8 acknowledgment gate. Read it when actually building.

Before you start: find the SSP and the data classification

Two inputs gate everything. Get them before writing code; if either is missing, flag it first rather than guessing.

1. The maximum data classification the feature will process (confirm with the data owner). This sets the GA-1/GA-2 routing ceiling. Don't assume "it's just a chatbot, it's low-risk" — a public assistant that can be fed CONFIDENTIAL case data is a CONFIDENTIAL system.

2. The System Security Plan (SSP) selects which controls apply at which level and sets parameter values. GA-5 carries two agency-defined parameters the catalog leaves blank:

| Control | Parameter | What the SSP defines | |---|---|---| | GA-5 | ga-5prm1 | The approved model formats for open-weights models (e.g. safetensors) | | GA-5 | ga-5prm2 | The approved model loaders for open-weights models |

Ask for the SSP's values; do not invent an approved-format or approved-loader list. If no SSP exists yet, flag that — the sibling skill ssp-navigator determines whether the Gen-AI overlay applies and at which level each GA control sits for this system. GA-5's parameters only matter if you self-host open-weights models; a pure API integration can mark GA-5 not-applicable (and should say why).

Procedure

Work in this order. For setup, apply each step. For audit, verify each and record pass / fail / not-applicable-per-SSP with evidence (the route config, the signed agreement, the loader version, the eval report, the UI screenshot). Recipes for every step are in references/implementation-recipes.md.

Stage 1 — Decide where the model runs (GA-1, GA-2, GA-3, GA-4)

1a. Route by data classification (GA-1/GA-2). Overseas-hosted GenAI APIs may process only up to RESTRICTED / SENSITIVE NORMAL; Singapore-hosted only up to CONFIDENTIAL / SENSITIVE HIGH. Make this a code invariant: tag each call site with the max classification it can carry and refuse to dispatch above the route's ceiling — don't leave it to a comment or a wiki page. Watch the cross-region trap: an "SG region" endpoint that can fail over or infer overseas counts as overseas (GA-1) for classification.

1b. Get the no-log/no-train agreement (GA-3). You need a legally-binding provider commitment: no logging, storage, or retention of inputs/outputs, and no training on them — covering all endpoints, parameters, outputs, and models you call. Prompt caching is allowed only if TTL ≤ 24 hours. GCC 2.0 GenAI API services satisfy GA-3 — prefer them and you skip the bespoke agreement; for any other provider, the signed agreement is the audit evidence.

1c. Clear the environment for self-hosted models (GA-4). If you self-host, the serving environment must be authorized for the highest data classification in the system it serves (per the relevant IM8 SSP for that classification). Self-hosting in a container brings the Container Security family in for the image and cluster — pair with the container-security skill.

Stage 2 — Secure the weight supply chain (GA-5)

Open-weights only. The risk is arbitrary code execution at load time. Use an approved format (ga-5prm1 — commonly safetensors, a non-executable tensor container) and never torch.load an untrusted pickle checkpoint (.bin/.pt/.ckpt), which executes code while deserializing. Load via an approved, frequently updated loader (ga-5prm2) — loader CVEs are the attack path; pin by version/digest and take reviewed bumps. Pull weights from a trusted, access-controlled source and verify checksums.

Stage 3 — Guard the inputs (GA-6, DP-8)

3a. File-upload safeguards (GA-6) — only if uploads are enabled. Disable bulk/batch uploads, require per-file classification confirmation (rejecting anything above the route ceiling), run DLP on extracted text before it reaches the prompt, and validate type/size. Treat extracted document text as untrusted (indirect prompt injection — test under GA-7).

3b. Classification disclosure at input fields (DP-8). Show the maximum permitted classification at or near every input field that reaches the model (and in the user guide), kept in sync with the actual route. Visible, not buried in help.

Stage 4 — Evaluate outputs (GA-7)

Build an evaluation harness with defined metrics, test scenarios, and criteria across three axes: accuracy (task correctness, RAG groundedness), safety (refusals, PII leakage, and prompt-injection/jailbreak resistance — direct and indirect), and quality (format, tone, no fabricated citations). Run it as a gate that re-runs after every model or prompt change, document the approach, and store results per model version to catch regressions. This is where prompt injection gets tested — it has no separate control.

Stage 5 — Inform users (GA-8)

Require an explicit acknowledgment of hallucination risk (a checkbox or "I Agree") before the app can be used — gate on it server-side, not just in the UI. Add a risk clause in the Terms of Use, a prominent persistent indicator that outputs are AI-generated, and educational material on responsible use.

Audit checklist

Verify each row; record evidence. "Param" marks the control whose pass criteria come from the SSP. GA-4/GA-5 apply only when self-hosting open-weights models — mark not-applicable-per-SSP with a reason otherwise.

| ID | Check | Where | Param | |---|---|---|---| | GA-1 | Overseas GenAI API only sends ≤ RESTRICTED/SENSITIVE NORMAL; enforced in code, not prose | Routing/dispatch code | | | GA-2 | SG-hosted GenAI API only sends ≤ CONFIDENTIAL/SENSITIVE HIGH; cross-region processing checked | Routing/dispatch code | | | GA-3 | Legally-binding no-log/no-train agreement covering all endpoints; cache TTL ≤ 24h; or GCC 2.0 | Contract / provider config | | | GA-4 | Self-hosted model environment authorized for the system's highest data classification | Hosting env / SSP | | | GA-5 | Open-weights loaded from an approved format (safetensors) via an approved, updated loader; no pickle | Model-loading code | ✓ | | GA-6 | Bulk uploads disabled; per-file classification confirm; DLP on extracted text; type/size validation | Upload handler | | | DP-8 | Max permitted classification shown at/near every model input field and in the guide | UI + user guide | | | GA-7 | Defined accuracy/safety/quality metrics + scenarios; safety incl. prompt-injection; gates on model change | Eval harness / CI | | | GA-8 | Explicit hallucination acknowledgment before access (server-enforced); ToU clause; prominent AI indicator | UI + ToU | |

Report per control ID with pass / fail / not-applicable-per-SSP, the evidence found, and — for failures — the fix from references/implementation-recipes.md. Where a pass depends on the SSP (GA-5 formats/loaders), ask for the SSP value rather than inventing one.

Related skills in this repo

• ssp-navigator — decides whether the Gen-AI overlay applies and at which level each

GA control sits (and where GA-1/GA-2 gate on data classification). Use it before declaring any GA control in or out of scope; this skill implements what it selects.

• secure-coding-as — Application Security (AS) + Crypto/Key Management (CK): where the

provider API key (AS-8) lives, and where prompt-injection fixes land as input handling and least-privilege tool design once GA-7 testing surfaces them.

• container-security — CS controls for a **self-hosted model's serving image and

cluster**; pair with GA-4 when you host open-weights models in containers.

• secure-pipeline — SD + SC: the GA-5 loader is a pinned dependency (SC-4) and the eval

harness is a required CI check (SD-3); wire GA-7 into the pipeline gate there.

• data-protection — the DP family this overlay leans on: DP-8 classification

disclosure (Stage 3), plus the residency and encryption constraints DP imposes on the data that GA-1/GA-2 routing (decided by maximum data classification) lets reach a model.

• security-testing — ST controls for the GenAI VAPT: the security-relevant findings

from GA-7 (prompt injection, unsafe or data-leaking output) feed the vulnerability programme and its remediation SLAs (ST-5) — accuracy/quality regressions stay in GA-7.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kyleoliveiro
• Source: kyleoliveiro/sg-gov-skills
• License: MIT

Install and usage instructions live in the source repository linked above.