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 the Public Sector
Govern every AI agent that serves citizens or handles records.
Averta gives public sector teams the guardrails to deploy AI on case files, citizen records, and benefits workflows. Classify every prompt and gate every action, so an agent's reach stays inside its mandate and citizen-facing decisions stay defensible.
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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.
Mandate enforcement
Keep agents inside their mandate, on every citizen interaction.
Every prompt, tool call, and response is classified and risk-scored at the execution boundary. Prompt manipulation and citizen-data leakage are caught before they touch a case, not after the records request lands.
Go to classification enginePolicy Framework
tool.writeEscalate
data.exportBlock
data.readAllow
Per-agent rights, enforced outside the model.
Defensible decisions
Make every citizen-facing decision defensible, with evidence on file.
Allowed actions live in policy your team owns, not in prompts scattered across product teams. Permissions scope by clearance, case, and minimum-necessary data, and every action ties back to a rule, an owner, and a tamper-evident record.
Go to tool policies frameworkSafe 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.
One platform for every layer.
Public sector 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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Sensitive data
Records Processing Agents
Control what records an agent can reach, retrieve, and expose, down to minimum-necessary access.
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Public-facing
Citizen support agents
Stop account takeover, PII leakage, and unauthorized actions in your customer-facing agents.
Read moreLearn 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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