How to Build an Open Model Deployment Safety Case

How to Build a Deployment Safety Case for an Open Model

Build an open-model deployment safety case by making one bounded claim about one named system, then connect its users, environment, threats, controls, tests, monitoring, incident response, rollback, owners, assumptions, and residual risk to reviewable evidence.

A safety case is an argument for a defined deployment. It is not a statement that the model is safe in general.

flowchart TD
    A["Bounded deployment claim"] --> B["System and context"]
    A --> C["Threats and harms"]
    A --> D["Controls"]
    D --> E["Test evidence"]
    E --> F["Monitoring and incident response"]
    F --> G["Residual risk and assumptions"]
    G --> H{"Independent release decision"}
    H -->|Approve| I["Limited deployment with change control"]
    H -->|Hold or reject| J["Revise, constrain, or stop"]

State the claim

Name the exact model, version, base or instruction-tuned form, conversion, quantization, runtime, prompt, retrieval, tools, safeguards, users, data, task, output, environment, period, and authority.

An example structure is: "This versioned system is acceptably controlled for internal drafting from approved public sources, with human review before any external use, for the stated pilot period."

Avoid claims such as "the model is safe" or "the safeguard prevents misuse."

Describe the system boundary

Map identity, access, prompt construction, source ingestion, retrieval, model execution, moderation, tools, output handling, logging, review, storage, and deletion.

Include third-party services, model hosts, adapters, data stores, queues, monitoring, and incident dependencies.

Open weights do not mean the whole system is local. A hosted runtime, remote embedding service, or external moderation API can change the data and security boundary.

Map users, bystanders, threats, and harms

Identify intended users, foreseeable misuse, represented people, data subjects, operators, reviewers, and anyone affected by outputs or actions.

Test unsupported claims, harmful advice, privacy exposure, bias, harassment, manipulation, impersonation, prompt injection, malicious retrieval, tool abuse, insecure output handling, denial of service, supply-chain compromise, and inability to obtain human help where relevant.

OWASP's Top 10 for LLM Applications supplies an application-security risk set. It is an input to the threat model, not a complete list of product harms.

Link controls to threats

For each material threat, identify prevention, detection, response, and recovery controls.

Controls may include data minimization, authorization before retrieval, prompt separation, tool allowlists, typed arguments, execution-time authorization, output validation, moderation, rate limits, human approval, logging, monitoring, incident playbooks, and rollback.

Publisher safeguards can be part of the control set. Meta's Purple Llama repository publishes safeguard and evaluation resources associated with Llama.

Test the exact version and configuration. A classifier cannot replace authentication, permissions, or recovery.

Produce system-specific evidence

Use representative, difficult, boundary, adversarial, multilingual where supported, long-context, unavailable-source, tool, and recovery cases.

Preserve inputs, sources, settings, outputs, scores, reviewers, disagreements, and failures. Measure task quality and safety together.

The official Llama 3.1 model card provides publisher evidence for the family. It does not test the adopter's data, prompt, retrieval, tools, users, infrastructure, or consequence.

Use independent reviewers for high-consequence boundaries.

Define monitoring and incident response

Monitor unsupported claims, unsafe outputs, refusals, sensitive-data events, prompt injection, safeguard results, tool errors, user reports, latency, failures, cost, and drift.

Define thresholds that narrow or stop the system. Name who can disable access, revoke credentials, disconnect retrieval, disable tools, switch models, restore records, notify affected people, and publish corrections.

Rehearse the incident and shutdown path before release.

Record assumptions and residual risk

Every safety case depends on assumptions. Examples include user training, source quality, language coverage, reviewer availability, logging, platform uptime, and unchanged model behavior.

State how each assumption is checked and what happens when it fails.

Residual risk should be described in terms of affected people, likelihood, severity, detectability, and available recovery. A green status without that explanation is weak evidence.

Make the decision independent of the builder

The release record should show the claim, evidence, failed gates, exceptions, owners, reviewers, approval scope, period, monitoring, rollback, and reevaluation triggers.

The NIST Generative AI Profile provides a cross-sector lifecycle frame for generative AI risk.

Legal, privacy, security, safety, domain, accessibility, and operational reviewers should assess the boundaries they own. The person who built the system should not be the only release authority.

Keep the case alive

Reevaluate when the weights, conversion, runtime, prompt, retrieval, sources, tools, safeguards, users, languages, environment, license, threat, or consequence changes.

For the wider deployment checklist, use [[How to Evaluate an Open Weight Model Before Deployment]]. For public assistant release testing, compare [[How to Test a Public AI Assistant Before Sharing It]] from episode 28.

This guide was developed with AI assistance from E027, NIST, OWASP, official Llama sources, and the linked safety-case framework. Dalton Anderson remains the author. Safety, security, privacy, legal, technical, model, domain, accessibility, operations, source, and founder review are mandatory before publication or deployment. Publication and deployment are not authorized.