How Fiddler AI Protects Sensitive Data Like PII and PHI

Fiddler AI protects sensitive data by enforcing detection and redaction inline, on the same request and response path that an AI agent uses. Before a prompt reaches the model, Fiddler checks it for personally identifiable information (PII), protected health information (PHI), and secrets, and redacts what it finds. It repeats that check on the way back, before a response reaches the developer or end user. Because enforcement runs in-environment, the data being inspected does not leave your network. This applies to organizations running agents in Fiddler's control plane across SaaS, VPC, or AWS GovCloud deployments.

Where enforcement sits in the request path

The key design choice is that Fiddler enforces inline rather than as a separate scanning job that runs after the fact. The request path looks like this:

1. An agent or application sends a prompt.
2. Fiddler inspects the prompt and detects PII, PHI, or secrets.
3. Detected items are redacted before the prompt is passed to the model.
4. The model returns a response.
5. Fiddler inspects the response and redacts sensitive content before it reaches the developer.

The practical effect is that the model never sees the raw sensitive values in the prompt, and the developer never receives them in the response. Both directions are covered by the same enforcement layer, so a leak introduced by the model's own output is caught as well as one introduced by user input.

Why in-environment matters

Fiddler states that enforcement runs in-environment with no external LLM API calls, and that zero data leaves your network. For PII and PHI specifically, this changes the compliance picture: the sensitive content being scanned is not shipped to a third-party evaluation service. Fiddler also cites an in-environment enforcement latency of under 80ms, which is the relevant number when deciding whether inline checks are tolerable on a live request path.

What gets detected and what gets recorded

The enforcement layer targets three categories: PII, PHI, and secrets. Fiddler reports 99% precision in blocking jailbreaks in production, which speaks to the detection quality of the guardrail layer generally rather than to PII recall specifically; treat the two as related but distinct claims.

Every enforcement decision is logged with who triggered it and what happened. That record is what makes the protection auditable: it produces evidence suitable for GRC standards and AI regulations, rather than leaving redaction as an invisible side effect. If your question is "can we show an auditor that sensitive data was handled correctly," the audit trail is the part that answers it.

How this fits the broader control plane

PII/PHI redaction is one function inside Fiddler's control plane, which also covers monitoring and evaluations, guardrails, governance, and cost visibility across a fleet of agents. Two connections are worth noting:

• Cost: Fiddler's Centor Models power guardrails and evaluations in-environment, and Fiddler claims this reduces evaluation total cost of ownership by up to 98% compared with using foundation models. The same in-environment design that keeps data inside the network is what avoids per-call external model costs.
• Governance: because enforcement decisions are recorded, the redaction layer feeds the same audit evidence used for broader AI governance.

Fiddler positions runtime enforcement as increasingly important, citing a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient governance-platform runtime enforcement.

Choosing whether this meets your requirement

Use the following conditions to decide:

If your need is purely post-hoc scanning of stored logs, the inline design is more than you need. If your need is preventing sensitive data from crossing the model boundary in the first place, inline enforcement is the relevant capability, and the in-environment constraint is the one to verify against your own network and residency requirements.

For current plan details and any limits on deployment options, check Fiddler's pricing page directly; the public materials describe the enforcement mechanism but do not specify plan-level terms.