Visibility explains what happened. Authorization decides what can happen.
VisIQ helps teams understand what their AI agents can access, control what they are allowed to do, and preserve verifiable evidence of every governed decision.
Research and practical instruments for understanding how agents acquire authority, how they fail under pressure, and how organizations can govern consequential actions before execution.
Featured research
Newest firstWhitepaper library
6 papers · full textFrom Monitor-First to Default-Deny
How to prevent unapproved tool execution in production
02Sensitive Knowledge and Retrieval Governance
How to stop AI agents from seeing more than they should
03Discover Before You Govern
Why AI governance starts with inventory, not policy
04Verifiable Decision Provenance
Why AI governance needs proof rather than logs
05Policy-Bounded Business Domains for AI Agents
How to keep AI agents inside approved business lanes
06Before You Press Send
Why the AI assistant in your browser already has your draft
Research instruments
Use the workAI security scorecard
30-question scorecard for evaluating any AI governance platform.
Open instrument →AI Security 66 Vendor Matrix
Feature-by-feature market research across the agent-security landscape.
Open instrument →Industry guides
Practical guidance for applying runtime authorization to regulated teams.
Open instrument →Healthcare guide
Govern retrieval and action where every disclosure has a compliance consequence.
Open instrument →Financial-services guide
Put approval and evidence around agent actions that move money or change risk.
Open instrument →Study the points where authority becomes action.
VisIQ studies the points where agents receive context, select tools, access systems, delegate authority, and create business side effects. We combine adversarial testing, runtime observation, policy evaluation, and evidence review to understand what agents can do, and what controls actually stop them.
Latest from the team
View the blog →Runtime Enforcement Is Table Stakes. Can You Prove What Your Agent Was Allowed to Do?
Blocking a bad action is now the baseline for AI agent security. The harder question is whether you can show, after the fact, what the agent was authorized to do and what the control decided before it acted.
ResearchAI Application Security Is Becoming a Full Lifecycle Discipline
Discovery, testing and runtime defense are now the accepted parts of the market. The part that matters is what connects them: a decision at the moment an agent’s proposal becomes a side effect, and proof that the decision happened.
ResearchThe Hugging Face Incident Was an Authority Failure, Not Just a Model Failure
The 2026 OpenAI–Hugging Face incident reads as a model story. The more useful lesson is architectural: agents composed individually trusted paths into an authority chain nobody explicitly approved.