Securing the Agentic Workforce

One platform to discover, test, secure and govern every AI agent across your organization.

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Announcement

ASA-SHIELD framework: measure the security maturity of your AI agents

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Platform

From discovery to governance, in four steps

ASA AI covers the full lifecycle of an agent: where it lives, how it can be attacked, how it is protected in production and how it stays compliant.

I

AI Discovery & Posture

Inventory of agents, models, MCP servers, tools and data access, with a risk score per asset.

II

Red Teaming & Attack Surface

Automated tests for prompt injection, jailbreak, data exfiltration and tool abuse.

III

Runtime Guardrails

Real-time analysis of inputs and outputs, blocking of abnormal behavior, human approval.

IV

Governance & Compliance

Decision traceability, alignment with OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001 and the EU AI Act.

Solution 01

AI Agent Security

How do you keep control of agents that read your data, call tools and act on your behalf?
Problem

AI agents act with broad permissions: they read documents, call APIs and chain actions. A malicious instruction hidden in content can hijack their behavior, without any traditional tool noticing.

Solution

ASA AI inventories your agents, attacks them in a controlled way before production, then monitors their actions at runtime. Behavior outside the defined scope is blocked or sent for human approval.

UsersRequests and documents
prompts
ASA AI guardrailsAnalysis · policy · logs
actions
AI agentsModels · tools · data
Solution 02

MCP Security

Which MCP servers do your agents use, and can they be trusted?
Problem

MCP servers give agents access to files, databases and services. An unverified server, tools with booby-trapped descriptions or overly broad permissions open the door to data leaks and command execution.

Solution

ASA AI discovers every connected MCP server, analyzes its tools and permissions, tests possible abuses and enforces a least-privilege access policy. Every tool call is logged and controllable.

AI agentTool request
call
ASA AI gatewayVerification · permissions · audit
access
MCP serversFiles · databases · APIs
Solution 03

Embedded AI

How do you test and secure AI features built into software you did not design?
Problem

AI assistants embedded in your SaaS tools behave like black boxes. You control neither the model nor its settings, yet they access your most sensitive data, with no runtime visibility.

Solution

ASA AI tests these features from the outside: prompt injection, indirect injection via shared content, data over-exposure, access control. At runtime, it flags abnormal access and responses.

EmployeesBrowser & desktop
prompts and data
ASA AI controlTests · monitoring · alerts
flows
SaaS assistantsCopilots · chatbots · agents
Company

Built for demanding environments

Self-hosted

Deploy ASA AI in your own infrastructure: your data never leaves your perimeter.

Role-based access

Separate the rights of security, compliance and development teams.

Audit log

Every decision and every block is traced and exportable to your SIEM.

See how ASA AI protects your agents.

A demo tailored to your AI environment.

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