Private AI
Private AI Assistant Security Requirements
Direct answer
A secure private AI assistant needs authenticated users, permission-aware retrieval, least-privilege tools, explicit data handling rules, prompt-injection defenses, verifiable outputs, safe logging, continuous evaluations, and tested incident and shutdown procedures.
Security is a property of the complete assistant, not the model endpoint. The application may connect identities, documents, CRM records, search indexes, tools, logs, and external providers; every boundary changes what could be exposed or altered.
Start with a threat model for one use case. Record the sensitive assets, authorized users, data flows, permitted actions, likely misuse, and business impact before selecting controls.
01
Control identity and access
Every retrieval and action should be tied to an authenticated identity. The assistant should never become a shortcut around permissions already enforced by source systems.
- Use centralized identity and appropriate multi-factor controls.
- Apply role, tenant, record, document, and field permissions before retrieval.
- Separate administrative, operational, and end-user privileges.
- Test access revocation and cross-tenant isolation.
02
Minimize data exposure
Document what data enters prompts, embeddings, indexes, logs, and vendor systems. Retain only what the use case requires and verify contractual and technical handling.
- Classify sources before connecting them.
- Remove unnecessary secrets and sensitive fields.
- Define retention, deletion, residency, and training-use requirements.
- Encrypt transport and storage using the organization’s standards.
03
Treat retrieved content as untrusted
Documents and web content can contain instructions designed to manipulate the assistant. Retrieved text should provide evidence, not silently redefine system policy or permissions.
- Separate system rules from retrieved content.
- Constrain tools independently of model instructions.
- Test direct and indirect prompt-injection scenarios.
- Require approval for external, destructive, financial, or high-impact actions.
04
Constrain tools and outputs
Tool calls create consequences beyond a mistaken answer. Validate every call in application code and design safe behavior when the model is uncertain.
- Use explicit allowlists, schemas, scopes, and rate limits.
- Separate read-only from write credentials.
- Add idempotency and validation to retryable operations.
- Return citations and mark uncertainty for consequential answers.
05
Log safely and evaluate continuously
Operators need enough evidence to reconstruct behavior without turning logs into an uncontrolled copy of sensitive company data.
- Record identity, source references, tool decisions, approvals, and outcomes.
- Redact secrets and unnecessary personal information.
- Evaluate permission compliance, citation support, refusal, and attack resistance.
- Run regression tests when models, prompts, tools, or sources change.
06
Prepare for incidents
Assume a credential will expire, a source will be poisoned, or an unsafe output will reach review. Recovery should not depend on the original developer being available.
- Provide pause, credential-revocation, and rollback controls.
- Define severity, escalation, communication, and evidence-preservation procedures.
- Practice source removal and index rebuilding.
- Assign accountable product, security, data, and workflow owners.
Evidence and sources
These primary references support the principles and implementation guidance in this article.
Frequently asked questions
Is an enterprise model automatically private?
No. Review provider terms and settings, but also assess application storage, retrieval indexes, access control, tools, telemetry, logs, support access, and deployment architecture.
What is the safest launch mode?
Begin with authenticated, read-only access for a narrow user group. Require citations, collect evaluation evidence, and add limited actions only after the answer workflow is reliable.
Can prompt injection be solved with a better prompt?
No single prompt is a complete defense. Use layered controls: treat content as untrusted, restrict tools in code, enforce permissions outside the model, validate actions, and require approval where impact warrants it.
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