Guide
2026 · Leebry team
Weak Access Control and Information Leakage
Potential Security Risks
Misleading Output and Hallucinations
Low Data Readiness
Automation vs. Human-in-the-Loop Balance
Verification Tax Trap
Most AI accuracy problems aren't really AI problems. They're rooted in what's underneath the model: weak access controls, undocumented security gaps, messy internal knowledge, and no clear answer for how much a human needs to check.
This guide is built from conversations with IT leaders at mid-market and enterprise companies, paired with input from our own engineering and customer success teams.
It covers six concerns in detail: access control and information leakage, security risk, hallucinations, data readiness, the automation-versus-human-in-the-loop balance, and the verification tax.
90%
of organizations deployed AI without extensively auditing internal knowledge
61%
of IT leaders report a gap between leadership expectations and what can be delivered
2%
trust AI outputs enough to act on most of them without review
Access control fundamentals
How to implement permission-aware retrieval, so AI tools only surface what a user is already authorized to see, before that gap becomes an incident.
A data readiness framework
A practical approach to cataloging, auditing, and assigning ownership over your internal knowledge base, the work most teams skip before deploying AI.
Where human review actually belongs
A framework for deciding what AI can handle alone versus what needs a person in the loop, and how to measure the real time cost, not just the time saved.
Guide to deploying AI you can actually trust
More resources
AI Readiness Assessment
5 min
AI at Work 2026
20 pages
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