PII in responses
Agents echo account numbers, emails, and personal data straight back to users, into logs, and across to downstream systems.
PII redaction
PII redaction for AI agent outputs.
PII redaction and AI DLP for AI agent outputs. Strip personally identifiable information, secrets, and harmful content from every response before it reaches a user, a log, or a downstream system.
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Trusted by teams securing AI in production
What leaks through without PII redaction.
The model produces a response and it goes straight to the customer. Without an output check, whatever is in that response leaves with it.
Secrets disclosure
API keys, tokens, and credentials that ended up in context get repeated in an output, where anyone can read them.
System prompt leakage
Probing pulls the agent's system prompt, rules, and hidden instructions into the open, handing attackers the map.
Harmful or off-brand content
The model produces toxic, unsafe, or non-compliant text that reaches a customer before anyone reviews it.
Support response
Output Classifier
PII redacted before send.
Your balance on card is .
PII detection and redaction
Strip PII before it leaves the agent.
PII detection on every response: names, emails, account numbers, and other sensitive fields are flagged and redacted in flight. PII never reaches a user, a log, or a downstream system that should not see it.
Production agent
Output Classifier
Secret detected and removed.
Here is the API key:
Sensitive data detection
Catch secrets before they reach a response.
Sensitive data detection finds API keys, tokens, and credentials that drift into the model context and removes them from outputs, so a secret in context never becomes a secret in writing.
Customer chat
Output Classifier
Unsafe content blocked.
Draft a reply that pressures the customer into sharing their password.
AI content moderation
Block harmful and non-compliant content.
AI content moderation on every output: toxic, unsafe, or off-brand text and system prompt leakage attempts are rewritten or blocked before they reach the customer.
Safe and customizable, without compromises.
Keep your data E2E encrypted
Protect agent workflows with end-to-end encryption, real-time redaction, and policy checks that block unsafe behavior in milliseconds while approved work keeps moving.
Policy-driven security
Define how agents handle data, tools, and decisions once. Averta applies those rules across every prompt, response, and action.
Adaptive data controls
Tune policies by team, use case, customer state, risk level, and tool permission without hardcoding guardrails into every agent.
What security teams are saying.
Before we started using Averta, we were hesitant to share sensitive information with agents. Averta changed that by providing the security and trust we needed, allowing us to significantly enhance our customer service experience.
Amir HaleemFounder at
HeliumThe decision layer in front of every action.
Classification, policy, access control, and audit working together as one AI agent security platform, protecting your agents internally and in production.
Classification Engine
Score every prompt for risk.
AI guardrails that score every prompt, tool call, and output for intent and risk before your model acts.
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Tool Policies Framework
Govern every tool call.
AI agent governance: define what each agent is allowed to do, enforce it on every tool call, attribution included.
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Audit & Observability
Every interaction recorded.
An AI audit trail of every prompt, tool call, decision, and output. Replay-ready, regulator-ready.
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MCP Gateway
Govern MCP tool access.
Expose only approved tools to each AI agent, through one governed MCP gateway.
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Averta Red Teaming
Pressure-test your agents.
Adversarial campaigns that simulate prompt injection, tool abuse, and data exfiltration on your production agents.
Read moreOutput classification, specifics.
What teams ask when they evaluate AI guardrails against their own production traffic.
PII redaction is the process of detecting and removing personally identifiable information from data before it leaves a system. For AI agents, that means scanning every model output for names, emails, account numbers, and other sensitive fields, then masking or removing them in flight, so PII never reaches a user, a log, or a downstream system.
PII such as names, emails, and account numbers; secrets and credentials; attempts to extract the system prompt; and harmful or non-compliant content. Each output is classified before it is delivered.
Depending on your policy, the output is redacted, rewritten, or blocked before it reaches the user. Sensitive data is removed in flight, not flagged after the fact.
On the response path, after the model produces an output and before that output reaches a user, a log, or a downstream system.
Yes. Output classification sits at the response boundary, independent of model and framework.
The Classification Engine classifies inputs and intent before the model acts. The Output Classifier classifies what the model produces, removing PII, secrets, and harmful content before it leaves. Together they cover both ends of the execution path.
No. Sensitive values are masked or removed while the rest of the response is preserved, so the user still gets a useful answer.
See Averta OS in action
Book a demo and see how Averta OS secures your AI agents from input to execution.
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