Best AI Security Practices for LLM Apps: A Production Checklist

A list of best ai security practices is only useful if it survives contact with a real production deployment. This guide collects the practices that experienced teams actually adopt, mapped against the canonical frameworks (OWASP LLM Top 10 ↗, NIST AI RMF ↗, MITRE ATLAS ↗) so you can defend each control during an audit. It is structured as a checklist by lifecycle phase rather than as a survey, so engineering and security leads can use it as a gap analysis against current state.

Phase 1: Threat Modeling Before Implementation

Practice	Why It Matters
Map every external input the model can see	Indirect prompt injection enters through retrieved docs, emails, web pages, MCP tool outputs
Classify each tool call by blast radius	”Read calendar” is not the same risk as “send email”; controls scale to consequence
Identify trust boundaries in agent loops	Agents cross boundaries silently — list every one explicitly
Document assumed-untrusted vs assumed-trusted data	Most LLM incidents trace to a trust assumption that turned out wrong

The single highest-leverage practice in this phase: write down which inputs are trusted and which are untrusted, and treat anything retrieved from the world as untrusted by default. The OWASP AI Security and Privacy Guide ↗ covers this framing in detail.

Phase 2: Defense-in-Depth at the Boundary

Practice	Tool Examples
Run a prompt-injection scanner on every input	Lakera Guard ↗, LLM Guard, NeMo Guardrails
PII redaction on inputs and outputs	Nightfall AI, Microsoft Presidio, LLM Guard scanners
Output validation for structured contracts	Pydantic schema enforcement, Guardrails AI
Rate limit per identity, not per IP	Standard API gateway pattern, scoped to user

A guardrail at the boundary catches the high-volume, low-sophistication attacks that make up the majority of injection attempts in the wild. It will not stop a determined attacker on its own — that is the point of defense in depth. For benchmarked guardrail comparisons see our AI firewall and guardrail solutions comparison; offensive technique coverage is on aisec.blog ↗.

Phase 3: Pre-Deployment Adversarial Testing

Practice	Why
Run Garak or PyRIT in CI against every model change	Catches regressions where a fine-tune reopens previously-fixed jailbreaks
Maintain a private prompt-injection corpus	Public corpora train models that pass them; private corpora catch real risk
Score releases on attack success rate	Numeric gate is harder to argue with than “felt safe”
Red team ↗ the application, not just the model	Most exploitable bugs live in the wrapper, not the weights

A practice often skipped: re-run the full adversarial suite after every prompt template change. Template edits are responsible for a large fraction of post-deployment regressions. See our coverage of open-source LLM security testing for tooling specifics.

Phase 4: Identity, Authorization, and Secrets

Practice	Pattern
No long-lived service accounts for agents	Workload identity + short-TTL tokens
Per-action authorization checks	Cerbos, OpenFGA, Auth0 FGA
Tools run with the minimum scope needed	One narrow API key per tool, not one broad key
Secrets never visible to the model	Secret broker injects credentials at tool-call time

The principle: an injected prompt that captures the agent’s credentials should still not be able to do significant damage. If your current architecture would fail this test, fix identity before adding more guardrails.

Phase 5: Runtime Observability

Practice	Tool Examples
Capture full traces of every agent run	Langfuse, Arize Phoenix, Helicone
Alert on anomalous tool-call patterns	Custom rules over trace store
Behavioral drift detection across model versions	WhyLabs, Phoenix evaluations
Sample and human-review production conversations	No tool replaces this

Most teams underinvest here until an incident forces it. The cost of trace storage is small; the cost of being unable to answer “what did the agent do during the breach window” is enormous.

Phase 6: Incident Response Specific to AI

Practice	Detail
Maintain a model rollback path	Point production to previous model + prompt template within minutes
Have a kill switch for tool use	Disable specific tools without taking the whole app down
Track AI incidents in a structured log	Helps detect repeat patterns; feeds the next threat model
Disclose responsibly when user data is affected	Standard breach disclosure applies; don’t treat AI incidents differently

Public AI incidents are tracked by ai-alert.org ↗; reviewing recent disclosures is a useful exercise for any team writing its own response playbook.

Phase 7: Governance and Compliance

Practice	Framework Mapping
Document risk controls against NIST AI RMF	Required for federal procurement and increasingly for enterprise
Track EU AI Act applicability per use case	High-risk classifications drive substantial controls
Bias and fairness testing where applicable	Required by sector regulators in finance and HR
Vendor AI risk reviews	Treat third-party AI like any other third-party software

Governance is not optional for organizations subject to regulator scrutiny. The frameworks are still maturing but the direction is set. For governance platform options see Holistic AI and similar platforms.

What to Skip

A few practices appear on most “best practices” lists but contribute little:

• Banning specific words at the prompt boundary. Trivially bypassed; high false-positive rate.
• Single-vendor “AI security platform” lock-in. The space moves too fast; modular stacks adapt; monolithic ones don’t.
• Static rule-only defenses. Static rules have a place in the stack; on their own they age poorly.

Update Cadence

This checklist is reviewed quarterly. The largest changes between versions tend to come from new attack classes (recent quarters: indirect injection via MCP tools, agent-loop drift, retrieval-side poisoning) rather than from new frameworks. The frameworks themselves update annually at most.

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Sources

• OWASP Top 10 for Large Language Model Applications ↗ — Vulnerability taxonomy that anchors most of the practices in this checklist.
• NIST AI Risk Management Framework (AI RMF 1.0) ↗ — Government risk management guidance increasingly referenced in enterprise procurement.
• MITRE ATLAS — Adversarial Threat Landscape for AI Systems ↗ — Adversary technique catalog providing the offensive perspective that defensive practices must address.
• OWASP AI Security and Privacy Guide ↗ — Comprehensive reference covering both AI security controls and AI-specific privacy considerations.