Definition: AI backup and model recovery cover all the steps you need to bring a machine learning system back to a known, trusted state. This applies after data loss, outages, ransomware or tampering. You back up more than the model weights. You back up every part needed to run the system and to rebuild it the same way. The goal is a recovery where you can prove nothing was changed.
Why conventional backup falls short
An AI system is more than one file. Say you only back up the server that runs a model. After an incident, you may be able to restart the service. But you often will not know which model version was trained on which data with which settings. Two beliefs are common, and both are wrong:
- "We can always retrain." Training often gives different results each time. It takes a long time and a lot of compute. And the original training data may be gone, changed or hit by the incident itself.
- "The model lives with our cloud provider." For your own fine-tuning, prompt settings and knowledge bases, you are usually the one who has to restore them.
What needs to be backed up
| Component | Examples |
|---|---|
| Model artifacts | Weights, checkpoints, tokenizers, fine-tuning adapters (such as LoRA) |
| Data | Versioned training, validation and test sets, labels, feature store |
| Code and environment | Training and inference code, pinned dependencies, container images |
| Configuration | Hyperparameters, thresholds, system prompts, guardrail rules |
| RAG components | Source documents, the vector database or the pipeline that can rebuild it |
| Metadata | Model registry, experiment tracking, release decisions, test results |
Tools such as MLflow (model registry, experiment tracking) or DVC (data versioning) help you link these parts in a consistent way. But they do not replace real backups on separate systems.
Typical recovery scenarios
- Ransomware: GPU servers, storage or registries get encrypted. What counts here are backups that cannot be changed and are kept apart from the production network.
- Data or model poisoning: Once you find the tampering, you must roll back to the last version that you can prove is older. For that, you need to know when each piece of data went into training.
- Faulty update: A new model gives worse results or acts in odd ways. A quick rollback to the last approved version limits the damage.
- Provider outage: An outside model is shut down or not available. A written fallback plan helps, such as a second model with its settings ready to go.
A practical approach
- Build an inventory: List every AI system with its parts and what it depends on, including outside services.
- Define RTO and RPO: For each system, decide how fast it must be back (recovery time objective). Also decide how much data loss you can accept (recovery point objective).
- Apply the 3-2-1 rule: Keep three copies on two different media, with one off-site. Add one copy that cannot be changed or is kept offline.
- Protect integrity: Store checksums and sign model files. Then you can check before a restore that nothing has changed. Prefer safe formats such as safetensors over pickle-based files, which can run code when loaded.
- Separate access: The people and accounts that manage backups should not be the same as those that run ML operations.
- Test recovery: Restore a full model in an isolated setting on a regular basis. Check it against reference test data. A backup you have never restored is a promise, not a control.
- Keep deletion duties in mind: If training data holds personal data, your deletion plan must cover backups as well (Art. 17 GDPR).
Regulatory context
Several rules call for backups:
- ISO/IEC 27001:2022: Annex A control 8.13 requires backup copies of information, software and systems, and asks you to test them on a regular basis.
- BSI IT-Grundschutz: In Germany, the module CON.3 describes the backup concept.
- NIS2: For organizations in its scope, Art. 21 names backup management and disaster recovery as part of cybersecurity risk management. This includes AI systems that are critical to the business.
- EU AI Act: For high-risk AI systems, Art. 15 requires robustness and resilience against errors and faults. It names technical redundancy, such as backup or fail-safe plans, as one way to do this.
Conclusion
You can only restore an AI system as well as its weakest protected part. And you can only trust it as far as you can prove the restored model is unchanged. SecTepe helps you bring AI systems into your business continuity and backup plan.
Frequently asked questions
How often should models be backed up?
At least for each approved model version and before each deployment. Keep versions of training data and settings too. Then every production version is documented and can be rebuilt.
Do I need to back up a third-party language model myself?
Usually not the base model. But you do need to back up everything you add: fine-tuning adapters, prompts, knowledge bases, integration code and settings.
More terms in “AI & Machine Learning”
- Adversarial Machine Learning
- AI Certification
- AI Data Protection Impact Assessment (AI DPIA)
- AI Forensics
- AI Governance for Managing Directors
- AI Governance Framework
- AI Incident Response Playbook
- AI Monitoring and Anomaly Detection
- AI Penetration Testing
- AI Phishing Detection
- AI Poisoning
- AI Red Teaming