Every tool call is an attack surface
When agents can query databases, call APIs, send emails, and execute code, every tool becomes a potential vector for data exfiltration, unauthorized actions, or privilege escalation.
For Financial Services
Govern every AI agent that touches money or accounts.
Averta gives financial services teams the guardrails to deploy AI on accounts, payments, and customer records. Classify every prompt, enforce per-agent policies on every tool call, and produce audit-grade evidence for SOC 2, PCI DSS, and DORA.
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Trusted by teams securing AI in production
Tool access is the risk.
Every customer-facing and internal AI agent fails in the same predictable ways. The attack surface is the same wherever they run.
Agents have too many permissions
Most agents are deployed with broad tool access for convenience. A customer service agent with database write access or an analytics agent with email capability creates unnecessary risk.
MCP and function calling expand the surface
Model Context Protocol and function calling make it easy to connect agents to tools. They also make it easy for compromised agents to abuse those connections.
Production agent
Classification Engine
Input classified across every layer.
Prompt injectionJailbreakData exfiltrationIntent
Summarize this ticket and issue the refund on file.
Account & PII safety
Keep account-takeover and PII leakage off your incident report.
Every prompt, tool call, and response is classified and risk-scored at the execution boundary. Social engineering, injected instructions, and PII leakage are caught before they reach your model or your customers, not after the transaction posts.
Go to classification enginePolicy Framework
tool.writeEscalate
data.exportBlock
data.readAllow
Per-agent rights, enforced outside the model.
Regulator-grade evidence
Govern money movement with the audit trail SOC 2, PCI DSS, and DORA expect.
Allowed actions live in policy your security team owns, not in prompts scattered across product teams. Permissions scope by customer, channel, and risk tier, and every action ties back to a rule, an owner, and a tamper-evident record.
Go to tool policies frameworkBuilt 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.
One platform for every layer.
Financial services use cases
Classification, policy, and audit working together as one AI agent security platform, protecting your agents internally and in production.
Internal AI
Employee copilots
Protect the internal assistants your team relies on, before they act on a poisoned document or over-reach into company data.
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Customer-facing
Customer support agents
Stop account takeover, PII leakage, and unauthorized actions in your customer-facing agents.
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Money movement
Agentic payments
Protect agents that can initiate, approve, or influence money movement.
Read morePowering 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
Learn from the team shaping AI security.
Research, guidance, and frameworks for security and engineering teams deploying AI agents in production.
Red teaming, 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.
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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