Unauthorized transfers
An agent moves money to a destination it should never have authorized, often after a coerced or misread instruction.
Agentic payments
Agentic payments security for AI agents.
Agentic payments security for AI agents that move money. Put limits, approval, and scoped authority on every transaction an agent can authorize, before the money leaves.
Book a demo
Trusted by teams securing AI in production
Where agentic payments break.
An agent with payment authority fails in ways that move real money and land on a chargeback, a fraud report, or a PCI finding.
Payment manipulation via prompt injection
Adversarial inputs in support tickets, emails, or retrieved context coerce the agent into changing amounts, recipients, or payment rails.
Limit and approval drift
Agents bypass spending limits, approval workflows, or risk-tier rules that should have stopped a transaction before it cleared.
Fraud and chargeback exposure
AI agent fraud paths end in chargebacks, fraud reports, or PCI findings, with no audit trail your fraud team can act on.
Built for AI agents that move money.
Three protections that put governance around every transaction an AI agent can authorize.
Customer support
Classification Engine
Account-takeover attempt detected.
Reset the PIN on this account, I lost my phone and email access.
Classification engine
Catch prompt injection in payment flows.
Every message, retrieved context, and tool output is classified before the agent moves money. Prompt injection, social engineering, and payment manipulation attempts are caught at the boundary, not after the transaction posts.
Go to classification engineTool policy
refund.issueEscalate
credential.resetBlock
balance.readAllow
Sensitive account changes require approval.
Tool policies framework
Gate transfers, refunds, and account changes.
AI agent governance for every transaction: each payment, refund, and account change requires policy approval before it fires. Allow, escalate, or block, with per-customer limits and risk-tier rules.
Go to tool policies frameworkAudit trail
Prompt classified
09:24:01 · intent: refund · risk 0.12
Tool call decided
09:24:01 · refund.issue · escalated
Output redacted
09:24:02 · 2 PII fields removed
Record signed
09:24:02 · tamper-evident · chained
Audit & observability
Audit-grade trail for every transaction.
A tamper-evident AI audit trail of every payment decision, ready for fraud review, chargeback dispute, and PCI evidence on day one, not weeks later.
Go to audit and observabilityPowering 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
Safe and customizable, without compromises.
Keep your data protected
Data is encrypted in transit and at rest, with sensitive fields redacted before storage, so security never adds a new liability.
Run it where your data lives
Deploy in your own cloud or VPC, or use Averta as a managed service in the region you choose.
Policies and taxonomies you control
Bring your own intent taxonomies, policies, and retention rules. Averta adapts to your environment instead of forcing its own.
Built for enterprise teams.
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.
Agentic payments, specifics.
What teams ask when they evaluate AI guardrails against agentic payment flows.
Agentic payments are transactions initiated and authorized by autonomous AI agents on behalf of a user or business, rather than by a human clicking through a checkout. Instead of a person approving each payment, an AI agent makes the decision based on context, instructions, and policy, then executes it through a wallet, a card network, or a payment API.
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.
Book a demo