Security is the deal-blocker
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 Inference Providers
Give your customers the AI guardrails they need
Embed Averta into your inference platform and give every customer prompt injection protection, PII redaction, and tool controls, so they can secure the agents they build on you.
Book a demo
Trusted by teams securing AI in production
Your customers are building agents. They need them secured.
Every customer-facing and internal AI agent fails in the same predictable ways. The attack surface is the same wherever they run.
You are the natural enforcement point
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.
Guardrails make customers stickier
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.
Built-in guardrails
Make AI guardrails a feature of your platform, not your customer's problem.
Every prompt, tool call, and response their agents make is classified and risk-scored at your inference boundary. Your customers get prompt injection protection, PII redaction, and risk scoring as part of your platform, with no integration work on their side.
Go to classification enginePolicy Framework
tool.writeEscalate
data.exportBlock
data.readAllow
Per-agent rights, enforced outside the model.
Native tool governance
Offer every customer tool-level controls, on day one.
Allowed actions live in policy that ships with your platform, not in prompts your customers have to write themselves. They get per-agent tool permissions, scoped by user, session, and data, the moment they sign up, and you get a security story enterprise buyers stop pushing back on.
Go to tool policies frameworkPowering 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
See how Averta runs inside your platform and what it takes to offer guardrails to your customers.
Book a demo