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A workable AI-security program should mature in sequence: first secure one application, then make its controls repeatable across the AI estate, and only then add protections designed for autonomous agents. F5’s 30-60-90-day framework uses that order to connect model testing, guardrails, incident response and agent oversight. In the first month you establish a tested baseline; in days 30–60 you operationalize ownership, patching and SOC response; in days 60–90 you add linked observability, tool permissions and human approvals for agents that can act.
What does the 30-60-90-day AI-security framework cover?
The framework is a dependency plan rather than three independent projects. A team cannot sensibly automate fleet-wide response until it knows how to test one application, and it should not apply request-and-response controls to an autonomous agent that can reason through several steps and invoke tools. Each phase therefore produces evidence and operating habits required by the next.
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| Phase | Timing | Primary outcome | Controls and evidence |
|---|---|---|---|
| Foundation | Days 0–30 | One application with a measured security baseline | Model selection, bare-model red team, application build, combined-system red team, four-category guardrails, staging validation and a repeatable zero-day loop |
| Scale | Days 30–60 | Repeatable controls across the AI estate | Risk-tiered test cadence, guardrail rollout and rollback, fleet-wide zero-day response SLAs, SOC enablement, an owner and last-test date for every application |
| Autonomy | Days 60–90 | Agents that can be reconstructed, constrained and approved | Linked thought/tool/decision observability, thought injection for risky reasoning, action-level tool permissions and approval gates |
F5 describes the ordering plainly: “Each phase builds on the one before it, so teams develop the right disciplines in the right order.” Treat that as a sequencing rule when deadlines tempt you to jump directly to agent controls.
How should the first 30 days secure a single AI application?
Choose one real application and use it to establish a complete test-and-control cycle. The first month is not a paperwork exercise: it compares the model by itself with the finished application, then turns findings into guardrails that can be maintained.
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1. Select the model and define the application boundary
Record the candidate model, version, hosting arrangement, data sources, users and intended actions before testing. State what the application is allowed to answer or do, what information it may receive, and which outcomes are unacceptable. A clear boundary makes later red-team results attributable to the model, the application logic or an integration.
2. Red-team the bare model for a baseline
Attack the unwrapped model before adding prompts, retrieval, tools or business logic. Use the threats that matter to the intended use case, including attempts to elicit restricted content, bypass instructions or manipulate the model into unsafe outputs. Preserve the prompts, model version, responses and severity decisions so the baseline can be compared with the combined system.
3. Build the application and test the combined system
Once the application is assembled, repeat the red-team exercise against the full path: user input, orchestration, retrieval, model, output handling and any connected service. The application can reduce a model weakness, leave it unchanged or introduce new attack paths through context, permissions or data handling. The comparison is the point; a model that looked acceptable alone is not automatically safe in production.
4. Design guardrails in four categories
- Existing regulations: Apply privacy, security, sector and consumer-protection obligations that already govern the business process.
- AI-specific regulations: Add requirements that target automated decision-making, transparency, documentation or prohibited uses in the jurisdictions where the application operates.
- Use-case threats: Address abuse paths specific to the workload, such as data exfiltration from retrieval, unsafe automation or manipulation of a customer-facing workflow.
- Red-team findings: Convert observed attack strings and failure modes into enforceable checks, filters, routing rules or access restrictions.
Guardrails should be treated as an operating layer, not a one-time prompt edit. Version them, associate each rule with the threat it addresses and keep a known-good release available for rollback.
5. Validate in staging before production
Replay the baseline attacks and representative business tasks in a staging environment. Check that a new guardrail blocks the harmful behavior without breaking legitimate requests, retrieval quality or required workflows. Record the test set, pass/fail decision, guardrail version and approver; these records become the first reusable assets for the estate.
6. Establish the zero-day loop
When a new attack technique or model weakness appears, use a fixed sequence rather than an emergency improvisation:
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- Patch the relevant guardrail or control and assign a new version.
- Validate the patch in staging against the new exploit and regression tests.
- Redeploy the approved version and monitor for failures or bypasses.
F5 CTO James White calls this output “a repeatable loop: when a new zero-day threat surfaces, red team again, patch the guardrails, validate, and redeploy.” The loop is the deliverable of the first phase, not merely the test report.
How do you scale controls from one application to dozens?
Days 30–60 turn the first application’s practices into a service that other teams can consume. Scaling means standardizing what can be standardized while allowing testing depth and response time to reflect risk.
Set risk-tiered red-team cadences
Classify applications by factors such as data sensitivity, user reach, business impact and the autonomy of their outputs. Assign each tier a red-team cadence, trigger conditions for an out-of-cycle test and an accountable approver. The framework does not prescribe one interval for every organization; the important property is that frequency follows risk and is recorded.
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Build a guardrail patch pipeline
Store guardrail policies like deployable code. The pipeline should test a candidate update, identify which applications consume it, roll it out in controlled waves and support a rapid rollback to the last approved version. A central release record should show the policy version, affected applications, validation results and rollback status.
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A vulnerability in a shared model, connector or guardrail can affect many applications at once. Set service-level agreements for triage, containment, patch validation and redeployment, with escalation when an application owner cannot meet the deadline. Track the SLA from detection to verified protection rather than stopping at the moment a ticket is closed.
Enable the security operations center
SOC analysts need AI-specific telemetry and playbooks, not just a new alert queue. Give them a way to distinguish prompt abuse, policy bypass, data leakage and suspicious tool use; show the application, model and guardrail versions involved; and document when to isolate an application or revert a policy. Run an incident exercise so analysts can investigate an AI event before a production incident tests the process.
Prove inventory, ownership and testing status
Create a living inventory that answers two questions for every AI application: who owns it and when was it last tested? F5 identifies those answers as the completion test for this phase. If either field is missing, the estate is not yet governable, regardless of how sophisticated an individual application’s controls appear.
Why do autonomous agents need different security controls?
An agent can run continuously, reason through a multi-step plan and take actions through tools. A filter applied only to the initial request and final response cannot show what happened between those points or reliably stop a dangerous intermediate decision.
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Link every thought, tool call and decision
Implement observability that ties an agent run together as one trace. Capture the sequence of reasoning events, tool invocations, returned data, policy checks and final decisions with timestamps and identifiers. Access to detailed traces should be limited to authorized investigators, but the record must be complete enough to reconstruct an incident after the fact.
Use thought injection when reasoning turns risky
When monitoring detects a dangerous plan, thought injection can replace the unsafe next step with a safer instruction while preserving legitimate task progress. For example, an agent attempting an unapproved change can be redirected to gather information or request authorization instead of executing the change. This is an intervention in the reasoning path, not merely a warning shown after the action.
Constrain tools at the action level
Give each tool the minimum permissions required for a specific task. Separate read, write, delete, financial, communication and administrative capabilities; scope credentials to the resource and duration; and enforce policy before every invocation. An agent should not inherit a broad user or service identity simply because it can formulate a plausible request.
Require approval for high-impact actions
Define which actions require a person to approve them, such as irreversible changes, external communications, access grants or transactions. The approval gate should display the proposed action, affected resources, rationale and relevant evidence, then record who approved or rejected it. Low-risk, reversible steps can continue automatically while consequential steps pause for review.
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What should the 90-day implementation schedule look like?
Use the following schedule to turn the framework into accountable work packages. Adjust staffing and exact dates to the application, but preserve the dependencies.
- Days 0–10: Choose the pilot application, document its boundary and model, assign an owner, and prepare a baseline red-team set.
- Days 11–20: Run bare-model and combined-system tests, categorize findings, and draft guardrails across the four required categories.
- Days 21–30: Validate in staging, release the first approved guardrail version, and rehearse the red-team/patch/validate/redeploy loop.
- Days 31–40: Inventory the remaining AI applications, assign risk tiers, owners and initial last-test dates, and define test triggers.
- Days 41–50: Implement the guardrail pipeline with staged rollout and rollback, then set zero-day response SLAs.
- Days 51–60: Connect AI events to SOC workflows, run an investigation exercise and close inventory gaps.
- Days 61–75: Select agent use cases, instrument linked traces for reasoning, tools and decisions, and test reconstruction with simulated incidents.
- Days 76–90: Enforce per-tool permissions, configure thought-injection interventions, add approval gates for high-impact actions and review the resulting evidence with security and business owners.
What commonly fails when organizations try to scale AI security?
- Testing only the model: Application prompts, retrieval data and integrations can add attack paths. Always compare bare-model and combined-system results.
- Writing static guardrails: Threats change and shared controls need coordinated updates. Version, deploy and roll back guardrails through a pipeline.
- Using one test schedule for every application: A uniform cadence can under-test high-impact systems or waste effort on low-risk ones. Tie cadence and escalation to risk.
- Leaving ownership implicit: Security cannot patch or retire an application when no team is accountable. Require an owner and a last-test date in the inventory.
- Applying chatbot controls to agents: Final-response filtering does not reveal or stop intermediate tool use. Trace the full run and enforce permissions before each action.
- Automating irreversible actions: A plausible plan can still be harmful. Put approval gates around high-impact operations and make the decision auditable.
How do you know the program is ready after 90 days?
Readiness is demonstrated by operating evidence, not by the existence of a policy document. One application has a measured baseline and a rehearsed zero-day loop; the estate has risk-based testing, deployable guardrail versions, response SLAs, SOC procedures and an owner plus last-test date for every application; and agents produce reconstructable traces while permissions and approvals constrain what they can do. If any of those proofs is absent, continue the relevant phase before expanding autonomy.
The framework’s central advantage is its order: establish a secure application discipline, make it repeatable across the fleet, then design for agents that act. That sequence limits unmeasured complexity while giving security, engineering and operations a shared set of handoffs.
Frequently Asked Questions
Can a small team use the framework without a dedicated AI-security department?
Yes. Assign explicit responsibilities even when one person wears several hats: an application owner, a security reviewer, an operations contact and an approver for consequential actions. Keeping those roles visible prevents work from disappearing between engineering and security queues.
How should low-risk applications be handled when testing capacity is limited?
Place them in a lower risk tier with a lighter scheduled cadence, but retain the same inventory, owner and last-test evidence. Trigger an earlier review when the data, users, model, tools or business impact changes.
What should a guardrail rollback record contain?
Keep the released policy version, deployment scope, reason for rollback, approving operator, time of change and validation that the prior version is active. Those details let investigators distinguish a failed patch from an unprotected application.
How can agent traces protect sensitive information while remaining useful?
Separate operational metadata from content, redact secrets where possible and restrict detailed traces to investigators with a defined need. Retention and access rules should be set before agents run in production so incident reconstruction does not create a second data-exposure problem.
Who should approve an agent’s high-impact action?
Use a person accountable for the business outcome, with security or compliance review when the action affects regulated data, privileges or external parties. Record the decision and the context shown to the approver so approval is meaningful rather than a blind click.
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