Choosing Open or Closed Models: What CISOs Must Know

As organizations race to infuse AI into their products and workflows, one strategic decision looms large: Should we use open-source AI models or closed-source (proprietary) models? For Chief Information Security Officers (CISOs) and technology leaders, this choice isn’t just about cost or performance—it’s about security, compliance, and risk management. The AI model you choose will dictate how much control you have, what threats you face, and what regulatory hoops you must jump through.

In this post, we’ll unpack the risks and benefits of open vs. closed models from a security and compliance perspective, compare on-premises open models like Meta’s LLaMA 2 with API-based services like OpenAI’s GPT-4, and share best practices for each approach. By the end, you’ll have the knowledge to choose the path that aligns with your organization’s risk appetite and regulatory obligations.

What Do “Open” and “Closed” Models Mean?

  • Open-Source Models are those whose architecture and weights are openly available (often under a permissive license). You can run them on your own hardware, fine-tune them, and modify their code. Examples include Meta’s LLaMA 2, EleutherAI’s GPT-J or GPT-Neo, and Stable Diffusion.
  • Closed-Source Models are proprietary: the provider does not release weights or detailed architecture. You access them via an API or platform only. Examples include OpenAI’s GPT-3/GPT-4, Anthropic’s Claude, and Google’s PaLM/Bard.

Note: There’s a spectrum—some vendors offer private hosted instances of their closed models (e.g., GPT-4 in your Azure tenancy), blending control with proprietary technology.

Security and Privacy: Data Control vs. Vendor Trust

  1. Data Control with Open Models

    • Keep all prompts, fine-tuning data, and inference on your infrastructure—ideal for sensitive or regulated data.
    • Eliminates third-party leakage risk (assuming no “phone home” in the model)⁽¹⁾.

  2. Privacy Concerns with Closed Models

    • Data traverses the vendor’s cloud: Where is it stored? Who can see it? Is it used to further train the model?
    • Mitigation: a strong Data Processing Agreement that forbids data retention or reuse beyond your requests⁽²⁾.

  3. Jurisdiction & Compliance

    • Open models let you choose hosting (e.g., EU-only servers to stay GDPR-compliant).
    • With closed models, verify regional hosting guarantees (e.g., Azure OpenAI in EU datacenters).

  4. Insider & Multi-Tenancy Risks

    • Open: trust your own admins.
    • Closed: trust the vendor’s staff and isolation controls (beware rare caching bugs that exposed other users’ chats)⁽³⁾.

  5. Service vs. Asset

    • Closed = third-party service (supply-chain risk if vendor is breached).
    • Open = in-house asset (you shoulder the infrastructure and security burden).

Model Behavior & Security: Alignment and Exploits

  • Guardrails & Safety

    • Closed models often ship with RLHF-based filters and continuous red-teaming (e.g., GPT-4 refuses bomb-making queries)⁽⁴⁾.
    • Open models require you to implement your own alignment layer—fine-tuning, prompt filters, and moderation pipelines.

  • Vulnerabilities & Updates

    • Closed: vendor patches vulnerabilities and updates you automatically.
    • Open: you decide when and how to update—risk of lagging behind community safety fixes.

  • Supply-Chain & Backdoors

    • Open: risk of hidden Trojans in community-trained weights—rely on trusted sources and hash checks.
    • Closed: opaque model, but vendor reputation and certifications provide some assurance.

  • Transparency vs. Obfuscation

    • Open: inspect weights, audit behavior, document training datasets yourself.
    • Closed: depend on vendor disclosures to meet EU AI Act transparency requirements⁽⁵⁾.

Compliance & Regulatory Considerations

  1. EU AI Act Roles

    • Provider vs Deployer: If you embed a closed model in your product, you may become the “provider” of a high-risk AI system under the Act.
    • Due diligence on vendor compliance is mandatory—otherwise you can be held liable for their shortcomings⁽⁶⁾.

  2. Technical Documentation

    • Open: you produce full design, data lineage, risk assessments (Annex IV).
    • Closed: request vendor’s documentation (training data summaries, adversarial testing reports).

  3. Licensing & IP

    • Open: confirm research-only vs commercial licenses (e.g., LLaMA 2 permits commercial use with attribution).
    • Closed: negotiate usage limits, data-deletion rights, and liability clauses in your contract.

  4. Certifications & Audits

    • Closed vendors may hold SOC 2, ISO 27001, or pursue AI Act conformity assessments—leverage these for faster compliance.
    • Open: plan your own third-party audits (e.g., NIST AI RMF alignment).

  5. Lock-in vs Flexibility

    • Closed = tied to vendor’s feature roadmap for traceability, explainability, or data-deletion.
    • Open = full control to implement new compliance features in-house.

Best Practices for Securing Open-Source Models

  1. Harden Your Hosting: OS patches, firewalls, least-privilege processes.
  2. Encrypt & Control Access: Disk-encrypt model weights; limit who can fine-tune or query.
  3. Safety Testing & Tuning: Fine-tune on refusal data; deploy input/output moderation layers.
  4. Stay Current: Monitor community advisories and upgrade when safe versions arrive.
  5. Robust Monitoring: Log every request/response with user IDs, timestamps, and anomaly alerts.
  6. Segmentation & Sandbox: Containerize or VM-isolate your inference servers.
  7. Verify Dependencies: Keep PyTorch/TensorFlow and other frameworks up to date; validate weight hashes.
  8. Failover Planning: Have a fallback (simpler model or rules engine) in case your primary system fails.

Best Practices for Using Closed-Source Models

  1. Vendor Security Assessment: Review SOC 2/ISO 27001 reports, encryption standards, and multi-tenant isolation.
  2. Contractual Protections: Insist on data-use clauses, data locality, and incident response obligations.
  3. Leverage Built-In Controls: Use system prompts, Anthropic’s “Constitution,” or OpenAI’s policy settings to enforce safety.
  4. Backend Gateways: Never call the API directly from client apps—route through your server to filter and log.
  5. Supplement with In-House Layers: Run open-source classifiers or rule-based filters around the vendor model.
  6. Backup & Continuity: Maintain a smaller local model or manual process as a fallback.
  7. Usage Monitoring: Track API key usage; alert on anomalies to detect stolen keys or abuse.
  8. Stay Engaged: Follow vendor release notes, participate in security webinars, and update your integration as new features arrive.

Weighing the Decision

  • Risk Appetite: Ultra-sensitive data → prefer open or on-prem closed. Rapid deployment & top capability → closed.
  • Resources & Expertise: Do you have MLOps and AI safety teams? If not, closed models offload that work.
  • Performance Needs: GPT-4-class quality still leads in closed models, but open offerings are catching up.
  • Compliance Trajectory: EU AI Act and GDPR favor vendors with conformity reports—but DIY gives maximum sovereignty.
  • Hybrid Approach: Many organizations use closed models for general tasks and open models for high-sensitivity workloads.

Conclusion

Choosing between open vs closed AI models is a strategic security decision akin to open-source vs proprietary software—except AI can directly handle sensitive data and decisions. There is no one-size-fits-all answer:

  • Open models grant full control, data ownership, and sovereignty but demand significant security, compliance, and alignment effort in-house.
  • Closed models deliver convenience, vendor-managed safety, and top performance, but require trusting a third party with your data and compliance obligations.

Whatever path you choose, apply due diligence: secure the model or integration, test exhaustively, monitor continuously, and align with current and upcoming regulations. The AI landscape evolves rapidly—stay informed, remain agile, and build flexibility into your strategy so you can pivot as capabilities and compliance frameworks mature.

Sources

  1. TechCrunch – Samsung bans generative AI like ChatGPT after data leak (employees pasted proprietary code)
  2. TechCrunch – Italy’s data protection authority bans ChatGPT over GDPR concerns
  3. Incidence of ChatGPT caching bug exposing other users’ chat histories
  4. Wired – DeepSeek’s Safety Guardrails Failed Every Test
  5. ISACA White Paper – EU AI Act obligations for transparency and adversarial testing
  6. EU AI Act, Article 15(5) & roles of providers vs deployers