Real problems Argorix turns into workflows
From discovering Shadow AI to producing audit-ready evidence, each use case maps directly to Argorix modules you can deploy as SaaS or on-premise.
Shadow AI Discovery
Teams adopt copilots, chatbots, APIs, and agents months before governance hears about them. The inventory in your GRC tool is a list someone typed once — the real estate lives in repositories, browser tabs, and CI pipelines nobody is watching.
- Continuous scanning across repos, SaaS traffic, CI/CD pipelines, and agent registries
- Every finding resolves to a system, an owner, a model, and a provider — not just a hostname
- Review queue moves each discovery from shadow to declared, sanctioned, or retired
AI Red Teaming
A model that passed review in March is a different system in September — new prompts, new tools, new data. Adversarial testing that happens once, in a document, tells you nothing about the app running in production today.
- Campaigns for prompt injection, instruction leakage, data exposure, and model manipulation
- Runs on a schedule and on every release, so drift shows up as a failed test, not an incident
- Every finding lands in remediation with an owner and becomes a reusable evidence object
Runtime Guardrails
Most AI policy lives in a document nobody can enforce at 3am. Guardrails puts the decision inline with the request — every prompt and every completion is evaluated, and the verdict is allow, redact, or block, in single-digit milliseconds.
- Inline checks for prompt injection, unsafe output, PII, and secret leakage in both directions
- Enforcement API and SDK drop in front of any provider — OpenAI, Anthropic, Azure, or self-hosted
- Human approval routing for the edge cases you don’t want decided automatically
Compliance Readiness
The EU AI Act, ISO 42001, and NIST AI RMF ask overlapping questions in different vocabularies. Answering each one separately means maintaining three spreadsheets that disagree by the second week.
- Map AI systems once, then satisfy multiple frameworks from the same control evidence
- Named owners and due dates per control, with remediation tracked to closure
- Board-level reporting generated from live control state, not a quarterly copy-paste
Evidence Readiness
Every audit starts the same way: someone spends three weeks screenshotting dashboards and chasing Slack threads to prove a control was working eight months ago. The proof existed — it just was never captured as an object.
- Findings, runtime events, policies, and red-team results captured as linked evidence objects
- Timestamped and hash-sealed at capture, so the pack proves what was true on that date
- One object satisfies several controls at once — assemble a pack instead of rebuilding it
Browser DLP for AI Tools
Employees paste contracts, customer records, and production code into AI chat tools every day. Network controls never see it — the request leaves an authenticated browser session over TLS to a domain your policy already allows.
- Managed extension inspects the paste before it leaves the tab — block, warn, or redact
- Approve or deny AI apps up front, with per-user and per-installation visibility
- Every sensitive event is logged with user, app, and matched pattern — ready for evidence
Agentic AI Security
A chatbot that gets manipulated says something wrong. An agent that gets manipulated calls a tool, moves money, or deletes a record. Controls that only inspect the final answer are looking at the wrong layer — the risk is in the steps.
- Controls bind to each step, tool call, and action — not just the final output
- Third-party agents scored with AIVSS and the MAESTRO 7-layer model before you let them in
- Memory and tool scope constrained per agent, so a poisoned step cannot escalate into the next
Safe Vibe Coding
Coding agents need context, so they take it: open files, environment variables, connection strings, and whatever else is in the buffer. Blocking the copilot kills developer velocity; allowing it ships your credentials to a third party.
- Redaction happens locally on the workstation — the secret never reaches the provider
- Values are restored in the response, so the developer keeps working code, not placeholders
- Managed policy packs per team, with a full redaction and restore audit trail per endpoint