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Use AI without losing
control of your data.
Your staff already use AI assistants, and your developers already run AI coding agents. Watch the agent, mask what staff paste into AI, and track where sensitive files go.
One problem:AI that sees more than it should.
SÝN ADR
Records the instruction behind what an AI agent did, not just the file it wrote. Decoys catch agents where they should not be.
Explore SÝN ADR →Mask secrets before they reach AIHLÍN
Catches personal, financial and health data, secrets and confidential records as staff paste them into AI tools, and redacts them first.
Explore HLÍN →Track where sensitive files goHILDR
Markers travel with a document and report where it surfaces, catching exfiltration and proving data policy is followed.
Explore HILDR →Govern it, classify it, build it safely.
AI security assessment
Control the AI your staff already use and secure the AI you build: shadow-AI discovery, data-flow mapping, prompt-injection testing and governance.
Learn more →Milind AI
Milind AI is a small language model trained on real DFIR, SOC, vulnerability assessment and penetration testing work. It is the engine that powers MSOC.
Learn more →A two-week shadow-AI assessment.
Inventory
We find the AI tools and agents actually in use, sanctioned or not, and what data they touch.
Measure
We show where regulated data, secrets and source code are reaching AI providers.
Recommend
A prioritized plan: which controls, which policies, and where SÝN ADR, HLÍN or HILDR fit.
What buyers ask.
We blocked AI tools. Isn't that enough?
Blocking moves AI use to personal devices, where you have no visibility at all. Visibility and policy work better than a ban.
We don't use AI agents.
Your developers probably do, unofficially. The assessment gives you the inventory either way.
We already have DLP.
Network DLP cannot see inside an AI prompt. HLÍN works at the keyboard, and HILDR travels with the document itself.
Find out what your AI can see.
Start with a fixed-scope, two-week assessment. Fully remote, Canadian-hosted.

