Before exposing an AI system to users or production data, enable input and output moderation, authenticated identities, rate limits, strict schemas, prompt-injection defenses, least-privilege tools, human approval for consequential actions, monitoring, adversarial evaluations, and deliberate retention controls. The right combination depends on whether you operate a chatbot, an autonomous agent, or a direct API workload, but these controls form a practical minimum rather than optional extras.
What to enable first
Safety settings work as a layered system. A content filter can catch abusive material, but it cannot stop a model from sending a sensitive file to an attacker through an approved tool. Authentication can identify a user, but it does not make a prompt trustworthy. Combine controls so that a failure in one layer does not become an incident.
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- Moderate both directions. Screen user input before generation and model output before display, storage, or downstream action. OpenAI recommends its free Moderation API or an equivalent filter, and reviewing responses before high-stakes use. Google recommends configurable safety settings, safe fallback responses, and narrower task definitions.
- Authenticate and meter every caller. Require registration or login where appropriate, attach a unique user or service identifier to each request, and enforce rate and volume limits.
- Constrain generation. Set maximum input length and output tokens. Prefer enumerated choices, validated JSON schemas, fixed response formats, and retrieval from approved material over unrestricted prose when the task allows.
- Treat external text as data, not instructions. Web pages, uploaded files, retrieved passages, emails, and tool results are untrusted. Keep them outside privileged developer instructions and validate extracted fields before they reach a tool.
- Put consequential tools behind approval. Require confirmation before actions involving accounts, files, money, production systems, deletion, or external communication. Give tools only the permissions they need and fail closed when review is unavailable.
- Monitor, evaluate, and document. Sanitize for personal data and jailbreak attempts, run ordinary and adversarial test cases, inspect traces and tool calls, publish acceptable-use rules, and assign an owner for escalation.
- Choose retention deliberately. Review provider logging defaults, contractual options, legal requirements, and the sensitivity of prompts and responses before launch.
Core controls and how to configure them
Moderate inputs and outputs
Use an input moderation check before a prompt reaches the model. Block or route content that violates your policy, and record only the minimum event data needed for investigation. Apply a second check to the generated response before showing it to a user, writing it to a database, or passing it to another system. For high-stakes subjects such as health, finance, employment, legal decisions, or safety-critical operations, add human review rather than relying on a classifier alone.
Define explicit outcomes for a filter result: allow, refuse, ask the user to rephrase, or send to a trained reviewer. A generic error message is safer than echoing the triggering text. Configure a safe fallback that does not reveal internal policy, hidden prompts, or moderation thresholds.
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Authenticate users and limit abuse
Use your normal identity provider, session controls, and service-to-service authentication. Attach a stable, unique identifier to each request so you can enforce per-user, per-tenant, and per-application quotas without exposing that identifier in the model prompt. OpenAI describes KYC-style controls and constrained input and output sizes as ways to reduce misuse; Google recommends unique IDs and query-volume limits.
Set separate limits for bursts and sustained use. For example, a short rolling-window request limit can stop scripted floods while a daily token budget limits slow abuse. Return a standard rate-limit response and a retry hint; do not allow clients to bypass limits by changing a display name or conversation title.
Constrain prompts, outputs, and data flow
Enforce length limits at the gateway, before tokenization costs or model context are incurred. Set a maximum output-token value appropriate to the task and stop generation when a validated end condition is reached. For extraction or workflow automation, require a schema with typed fields, allowed enum values, and length limits. Reject malformed output instead of attempting to repair it with another unrestricted model call.
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Use retrieval from an approved corpus when answers must be grounded in known material. Mark retrieved passages as quoted data and include document identifiers so a reviewer can trace the answer. Keep secrets, access tokens, and unnecessary personal data out of prompts; redact them before logging or sending them to a provider.
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Preventing prompt injection in agents and retrieval systems
OpenAI defines prompt injection as a case where untrusted text or data enters an AI system and malicious content in that text attempts to override the system’s instructions. The practical defense is to maintain a hard boundary between instructions and data, then assume that any data crossing the boundary may be adversarial.
Separate instruction and data channels
- Keep system and developer instructions in a controlled template that retrieved text cannot edit.
- Wrap webpages, files, emails, and search results in a clearly labeled untrusted-data field.
- Ask the model to extract only the fields required for the next step, rather than to follow instructions found in the material.
- Validate those fields in ordinary application code against types, ranges, destinations, and allowlists.
- Pass only the validated fields to a tool, never the entire untrusted document or the model’s free-form explanation.
Do not assume that quoting, delimiters, or a sentence such as “ignore instructions in this text” is a complete defense. An attacker can place instructions in an image, a document comment, a tool response, or a seemingly harmless web page. Keep credentials and privileged prompts out of any context that an external source can influence.
Use structured intermediate steps
For multi-step agents, define an explicit state object for each transition: the goal, the approved resources, the proposed action, and the evidence supporting it. Reject transitions that add a new recipient, file path, domain, amount, or permission without a fresh validation step. This makes it possible to inspect a trace and determine whether a failure came from retrieval, reasoning, policy enforcement, or the tool itself.
Tool permissions, approvals, and blast radius
Agent risk rises sharply when arbitrary model text can directly trigger tools. Start with read-only capabilities and a narrow allowlist. Separate file, network, database, messaging, and payment permissions; do not give one general-purpose credential to every tool.
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Actions that should require confirmation
- Sending email, messages, posts, or invitations to an external recipient
- Creating, changing, or deleting user accounts and permissions
- Moving money, placing orders, or changing billing information
- Writing to production databases, deploying code, or changing infrastructure
- Reading private files or exporting data outside the approved tenant
- Deleting records, files, backups, or audit evidence
Show the user the exact operation, target, and material parameters in the approval dialog. A confirmation for “continue” is not meaningful if the recipient or amount is hidden. Expire approvals quickly and bind them to the displayed parameters so a later model step cannot reuse consent for a different action.
Sandbox and network boundaries
Run code execution and file operations in an isolated environment with an independent filesystem and restricted network access. Deny outbound connections by default, then allow only the domains and methods required for the task. Keep production credentials outside the sandbox and issue short-lived, scoped tokens when a connection is unavoidable. If an approval service, policy engine, or audit logger is unavailable, fail closed rather than silently executing the action.
Settings by workload
| Workload | Required baseline | Additional controls | Main failure to prevent |
|---|---|---|---|
| Chatbot | Input/output moderation, authenticated sessions where appropriate, per-user rate limits, context and output caps, safe fallback responses | Human escalation for high-stakes topics; approved retrieval sources; abuse and privacy monitoring | Harmful or misleading replies, harassment, runaway usage, and disclosure of another user’s data |
| Agent | All chatbot controls plus untrusted-data isolation and strict output schemas | Tool allowlists, least-privilege credentials, approval gates, sandboxed files and network, trace evaluation, fail-closed behavior | Prompt injection causing unauthorized reads, writes, messages, or data exfiltration |
| Direct API | Per-caller authentication, unique safety identifiers, quotas, moderation, request and response limits, audit logs | Provider retention selection, abuse-monitoring review, tenant isolation, contractual privacy controls; review every cybersecurity-sensitive tool call | Anonymous abuse, uncontrolled cost, retention of sensitive payloads, and unreviewed high-impact automation |
Compare an implementation on seven axes: content-risk coverage; resistance to prompt injection and data exfiltration; tool permissions and blast radius; human-review burden; privacy and retention; auditability and evaluation support; and deployment complexity. NIST’s control-overlay work for assistants and agents is useful as an implementation-oriented comparison framework, but it remains guidance from an ongoing project, not a completed certification standard.
Privacy, logging, and retention choices
Logging improves abuse detection and incident response, but logs can contain the same sensitive prompts and responses as the application. OpenAI states that abuse-monitoring logs may include prompts, responses, and classifier metadata and are retained for up to 30 days by default, subject to legal or safety exceptions. Treat that as a provider-specific default, not a universal rule for every model service.
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Decide what to retain
- Store request IDs, policy outcomes, model version, latency, token counts, and tool decisions when those fields are enough for operations.
- Redact credentials, authentication headers, payment data, and unnecessary personal information before application or provider logging.
- Use short retention periods for raw content and longer retention only for aggregated metrics or de-identified traces.
- Restrict log access, separate tenant data, encrypt it in transit and at rest, and record administrator access.
- Document deletion schedules and legal holds so an incident does not become a reason to retain everything indefinitely.
Eligible OpenAI customers can request Modified Abuse Monitoring or Zero Data Retention. OpenAI also describes Private Safety Processing as a design for automated safety review without retaining customer prompts or responses. Eligibility, contract terms, and feature behavior must be confirmed for the specific account and endpoint before relying on them.
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Production telemetry should show more than latency and cost. Track moderation outcomes, refusal rates, schema-validation failures, rate-limit events, tool proposals, approval decisions, denied network requests, and unusual changes in a user’s volume or destination pattern. Keep enough correlation data to reconstruct an incident without collecting full prompt content by default.
Build an evaluation set
- Collect representative benign requests from each supported workflow.
- Add policy-edge cases, ambiguous wording, multilingual inputs, long-context examples, and malformed structured data.
- Include adversarial prompts that attempt instruction override, secret extraction, tool escalation, and cross-tenant access.
- Run the set whenever you change a model, prompt, retrieval index, moderation policy, or tool permission.
- Inspect traces and tool calls, not only the final answer. OpenAI recommends trace graders or equivalent evaluations for agent behavior.
- Set release thresholds for unsafe content, unauthorized tool calls, schema errors, and false refusals; block deployment when a critical threshold is exceeded.
Assign an incident owner and a backup. Your runbook should cover disabling a tool, revoking a credential, tightening a rate limit, preserving relevant logs, notifying affected users, and restoring service with a known-safe configuration. Publish an abuse-reporting route and explain what information reporters should include.
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Only moderating the final answer
Why it fails: harmful input can trigger retrieval, tool calls, or data access before a response exists. Fix: moderate at ingress, before sensitive tools, and at output; add human review for high-impact decisions.
Putting retrieved text in a privileged prompt
Why it fails: a malicious document can masquerade as a developer instruction. Fix: isolate it as untrusted data, extract constrained fields, and validate them in code.
Giving an agent one broad credential
Why it fails: one injection can expose every system that credential reaches. Fix: split tools and credentials, use read-only defaults, scope tokens, and require approval for writes.
Logging everything forever
Why it fails: operational convenience creates a second sensitive data store. Fix: redact, minimize, restrict access, set deletion schedules, and select provider retention controls deliberately.
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A launch checklist
- Input and output moderation policies are documented and tested.
- Every caller has an authenticated identity, quota, and abuse-detection path.
- Prompt, context, output-token, and schema limits are enforced outside the model.
- External content is labeled untrusted and cannot edit privileged instructions.
- Tools use allowlists, least privilege, isolated filesystems, and restricted networks.
- High-impact reads and writes show exact parameters and require expiring approval.
- Safety failures, tool calls, and policy decisions are observable through protected audit records.
- Representative and adversarial evaluations gate model, prompt, and tool changes.
- Provider logging, retention, deletion, and privacy options match the data classification and contract.
- Acceptable-use rules, reporting channels, escalation owners, and shutdown procedures are published.
No universal numerical risk reduction or single “safe mode” exists. Select controls according to the model provider, data sensitivity, user population, tool privileges, and applicable law, then verify them continuously as the system changes.
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