Account takeover via chatbot
A social-engineered chat session escalates into a privileged account session. The agent acts on behalf of the attacker.
Chatbot security
Chatbot security for AI support agents.
Chatbot security and AI agent governance for support. Block chatbot prompt injection, account takeover, and PII leakage before they reach your customers, your data, or your audit trail.
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Trusted by teams securing AI in production
Where chatbot security fails.
Customer-facing AI fails the same four ways wherever it runs. Each one ends in a security incident or an angry customer.
PII and account data leakage
Balances, identifiers, and personal data appear in agent responses, transcripts, or downstream analytics.
Chatbot prompt injection
Hidden instructions in user input or retrieved context coerce the agent into resetting credentials, releasing holds, or executing transfers.
Compliance drift
Agents give unauthorized advice, mishandle disclosures, or stray from regulator-mandated scripts.
Built for the way support actually runs.
Three protections that turn an AI support agent from a regulatory liability into a tool your auditors and your CISO can both sign off on.
Customer support
Classification Engine
Account-takeover attempt detected.
Reset the PIN on this account, I lost my phone and email access.
Classification engine
Catch account-takeover and prompt injection attempts.
Chatbot security starts before the agent acts. Every message and action is classified for intent and risk, so social engineering, prompt injection, and account-takeover attempts are caught at the door.
Go to classification engineTool policy
refund.issueEscalate
credential.resetBlock
balance.readAllow
Sensitive account changes require approval.
Tool policies framework
Gate refunds, resets, and account changes.
AI agent governance for every support action: refunds, password resets, account changes, and credit holds require policy approval before they fire. Allow, escalate, or block, with attribution.
Go to tool policies frameworkSupport response
Output Classifier
Secrets and PII stripped from response.
Account updated. We emailed a receipt.
PII redaction
Keep secrets and PII out of responses.
PII redaction on every response: account numbers, balances, and personal data are detected and stripped from agent outputs before they reach a user, a log, or a downstream system.
Go to PII redactionPowering safe AI execution at leading teams.
Cyfrin secures its production AI agents with Averta.
Book a demo“Averta gave our agents enforceable boundaries for the dev environment, so instructions like ‘don’t read .env files’ became policy instead of polite suggestions.”
Mikhail Karan
Head of Engineering
Govern the tools your support agents use.
Cloud, private VPC, embedded SDK, or gateway integration. Run Averta where your data, policies, and auditors need it.
Cloud (SaaS)
Fully managed by Averta. Fastest path to production, no infrastructure to run.
Private / VPC
Deploy in your own environment, so data never leaves your boundary.
Embedded SDK & Proxy
Drop Averta into your stack at the SDK or proxy layer, wherever your agents run.
Gateway Integration
Route agent traffic through the gateway, so policy and audit apply at the edge.
Support, specifics.
What teams ask when they evaluate AI guardrails against their own production traffic.
On held-out adversarial and benign traffic, with precision, recall, and false-positive rates reported per intent class and per risk band. You can run the engine in shadow mode against your own production traffic before enforcing anything.
Yes. Classification sits at the execution boundary, independent of model and framework. Switching providers or upgrading models does not change the policy surface.
They are escalated, blocked, or routed for review according to your policy. The default posture is to never allow an unclassified execution silently.
Yes. The taxonomy is configurable per product surface. Start from our generic baseline and extend it, or define one from scratch for a specific copilot or workflow.
Inline, ahead of the model and ahead of any tool execution. Inputs are classified before they reach the agent, planned actions before they fire, and outputs before they reach the customer.
Both terms describe the same job: a guardrails layer that inspects prompts and actions before they execute. Averta's Classification Engine is that layer for AI agents, scoring every input, tool call, and output inline so your policy layer can allow, escalate, or block.
Sensitive data is redacted in flight, so account numbers, balances, and personal data are stripped before anything is written to a log or store. Classification metadata and audit records are encrypted in transit and at rest, retained according to your policy, and never used to train shared models. Averta can run in your own cloud or VPC, or as a managed service in the region you choose.
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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