AI observability: Auditing and tracing AI decisions

Jessica BrennanSenior Product Marketing Manager | F5

This blog post is the eighth in a series about AI guardrails.

AI security teams are being asked to answer a new class of questions, often after something has already gone wrong. Why did an AI system allow this response? What policy was applied? Was sensitive data exposed? And can we prove it didn’t happen elsewhere?

As AI becomes a core component of the tech stack, regulators, auditors, and internal governance teams increasingly expect clear, defensible evidence of how AI decisions are made and controlled. That expectation is driving a shift from basic monitoring toward true AI observability.

Why AI observability matters

Traditional security tooling was designed for deterministic systems: applications, APIs, and infrastructure with predictable behavior. AI systems don’t behave that way. They reason probabilistically, adapt over time, and—especially with agentic workflows—can take actions that are difficult to anticipate or reconstruct after the fact.

This creates a visibility gap. Security teams may know that an AI interaction occurred, but not why a particular output was generated or how policy controls were applied in context. Without observability at the AI runtime layer, it becomes nearly impossible to demonstrate accountability or meet emerging regulatory expectations.

Observability at runtime

AI risk materializes at runtime, when models interact with users, applications, agents, or data. F5 AI Guardrails operates directly at this layer, providing continuous visibility into prompts, responses, and enforcement decisions as they happen.

Rather than relying on opaque, provider-specific controls, AI Guardrails delivers model-agnostic observability across public, private, and customer-hosted deployments. This ensures consistent insight regardless of which models or vendors are in use. Just as importantly, it gives security teams a unified view of behavior across AI applications, agents, and users without disrupting performance or developer workflows.

From visibility to explainability

Observability isn’t just about collecting logs. To support audits, investigations, and governance reviews, teams need to understand how decisions were made.

That’s where F5 AI Guardrails’ outcome analysis comes in. Outcome analysis provides step-by-step traceability for every enforcement action taken at runtime. For each AI interaction, teams can see:

• The input received by the model
• The policies evaluated in that context
• The reasoning behind the enforcement decision
• The resulting output delivered to the user

This level of explainability turns AI security from a black box into a system that can be examined, explained, and trusted. So. when questions arise, whether from internal stakeholders or external regulators, teams can reconstruct the full decision path with confidence, rather than relying on assumptions or incomplete logs.

Auditability built for compliance teams

Regulatory frameworks such as the EU AI Act and emerging AI governance standards place increasing emphasis on traceability and accountability. It is no longer sufficient to say that controls exist; organizations must be able to prove that those controls were enforced consistently over time.

F5 AI Guardrails generates audit-ready logs designed to support compliance workflows. Every AI interaction, policy evaluation, and enforcement action is captured in a structured, defensible format. This allows compliance and security teams to respond more efficiently to audits, regulatory inquiries, and internal reviews, without the hassle of assembling evidence from disconnected systems.

Because AI Guardrails integrates with existing SIEM and SOAR platforms, AI-specific telemetry can also be correlated with broader security signals, helping teams maintain situational awareness across the enterprise.

Traceability across complex AI workflows

Modern AI systems rarely operate in isolation. A single request may involve multiple models, tools, APIs, and autonomous agents acting on a user’s behalf. Without end-to-end traceability, reconstructing what happened, and where risk was introduced, can be extremely difficult.

F5 AI Guardrails provides traceability across these multi-step workflows, allowing teams to follow the chain of interactions from input to outcome. This is critical for incident response, root-cause analysis, and ongoing governance. And as organizations start to leverage agents, traceability becomes even more crucial.

With agentic AI, actions are distributed across multiple steps and tools that often span model calls, API requests, and data access. So, a single outcome may reflect a chain of decisions that must be reconstructed end-to-end for investigation and audit purposes.

Strengthening observability with adversarial insight

Observability improves when organizations understand not only how AI systems behave in production, but how they fail under adversarial conditions. This is where adversarial testing plays an important supporting role.

While AI Guardrails provides runtime observability and enforcement, insights from F5 AI Red Team can surface weaknesses that may not be visible through passive monitoring alone. Adversarial testing reveals how models respond to prompt injection, jailbreaks, and chained interactions that can lead to policy violations or unintended behavior.

Agentic fingerprints are particularly important in this context. They provide a traceable record of how an agentic attack unfolds across multiple turns, capturing decision paths, behavioral shifts, and tool usage so security teams can understand not just that a weakness was exploited, but how and why it emerged.

Findings from adversarial testing can then be translated into guardrails with observability data then validating how those controls perform in production. This feedback loop helps organizations move from reactive monitoring to continuous improvement, using observability not just to see what happened, but to reduce the likelihood of repeat issues.

A foundation for accountable AI

As AI systems become more autonomous and interconnected, the ability to observe, trace, and explain their behavior becomes essential. Visibility into individual interactions is no longer enough. Organizations must be able to reconstruct how decisions unfold across models, applications, agents, and users, especially when something goes wrong.

F5 AI Guardrails provides the observability layer needed to meet this challenge, delivering runtime insight, outcome-level traceability, and audit-ready records across AI workflows. When combined with adversarial insight from F5 AI Red Team, organizations gain a clearer understanding of both how AI behaves in production and how it can be pushed beyond expected boundaries.

Together, these capabilities support a more accountable approach to AI adoption—one where security teams can investigate incidents with confidence, compliance teams can demonstrate due diligence, and organizations can scale AI without sacrificing transparency or control.

Explore how F5’s AI runtime security solutions deliver observability and audit-ready traceability across models, applications, and agents.

Also, be sure to check out these previous blog posts in our AI guardrails series:

AI compliance and regulation: Using F5 AI Guardrails to meet legal and industry standards

Classifier-based vs. LLM-driven guardrails: What actually works at AI runtime

Responsible AI: Guardrails align innovation with ethics

What are AI guardrails? Evolving safety beyond foundational model providers

AI data privacy: Guardrails that protect sensitive data

Why your AI policy, governance, and guardrails can’t wait

AI risk management: How guardrails can offer mitigation

About the Author

Jessica BrennanSenior Product Marketing Manager | F5

More blogs by Jessica Brennan