Security Services

AI Policy & Operating Rules

Define clear rules for how your organization uses AI — covering approved tools, data boundaries, human review requirements, and escalation paths — before ad hoc adoption creates ungoverned risk.

Overview

AI policy work isn't about restricting innovation. It's about making adoption sustainable by defining boundaries that prevent data leakage, vendor lock-in, quality failures, and regulatory exposure before they compound into problems that are expensive to unwind.

This engagement produces operational AI policy documents covering approved tools and vendors, data classification rules for AI inputs, human review requirements for AI-generated outputs, escalation paths for edge cases, and the operating rules that make policy enforceable rather than aspirational. Policies are designed to be specific enough to follow and flexible enough to update as tools and capabilities evolve.

What This Covers

AI usage policy covering approved tools, data boundaries, and prohibited uses

Data classification rules for AI inputs — what can and cannot be processed by AI tools

Human review requirements for AI outputs in client-facing, financial, legal, and regulated contexts

Escalation paths for edge cases, failures, and situations where AI output quality is uncertain

Policy maintenance framework with review triggers and update schedule

Operational Outcomes

What stabilizes once AI usage has operating rules.

Employees have clear guidance on which tools are approved, what data can be processed, and where human review is required.
Data leakage risk from AI tools drops because classification rules define what information can and cannot enter AI systems.
Policy enforcement is practical because the rules are specific enough to follow — not a generic 'use AI responsibly' statement.

Your team is using AI tools and you need clear operating rules before ad hoc adoption creates ungoverned data, vendor, or quality risk.

Engagement Flow

Scope, validate, and follow through.

Security work should prove something useful, document it clearly, and make the next move easier to execute.

Scope & authorize

Clarify environment, boundaries, timing, and who sees results.

Test & document

Evidence gathered deliberately, findings written for operators and leadership.

Remediate & retest

Fix guidance, retest support, and recurring ownership when needed.

Remediation can cycle back to scope for periodic reassessment

Pressure Profile

Pressure patterns that usually point here.

Your team is using AI tools and you need clear operating rules before ad hoc adoption creates ungoverned data, vendor, or quality risk.

Scoping Conversation

Define the right depth, timing, and follow-through.

Discovery should clarify scope, environment, timing, reporting needs, and whether the next move is testing, recurring leadership, or a compliance engagement.

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