AI Impact Analysis
Where AI changes the risk
Employees may use public assistants, browser extensions and integrated copilots without a shared policy. Prompts, uploaded documents and connected data sources can expose confidential information if access, retention and provider settings are not understood. We map approved and unapproved use with your team, without assuming that every AI tool presents the same risk.
Generated code can introduce insecure patterns, vulnerable dependencies or logic errors. AI-assisted phishing and impersonation can also challenge existing verification procedures. We assess the relevant scenarios for your business, without treating hypothetical attacks as evidence of an incident.
Our Process Step by Step:
Discover actual use
Discover actual use
Agree the business scope, identify tools and owners, map the data shared with providers and review access permissions and integrations.
Assess controls
Assess controls
Review acceptable-use rules, information classification, supplier settings, human review of generated output and secure development practices. Technical testing is separately scoped and authorized.
Prioritize improvements
Prioritize improvements
Connect findings to realistic business consequences and identify proportionate safeguards, accountable owners and practical next steps.
What you receive
A documented AI use and exposure map, a prioritized risk register, control recommendations and an implementation roadmap. The agreed scope determines whether policy updates, developer guidance and a management workshop are included.
Business value
Support responsible AI adoption while reducing avoidable information exposure and rework. The assessment supports security decisions, it is not a certification or a guarantee of regulatory compliance.
Frequently Asked Questions:
Do we need an internally developed AI system?
No. Employee use of third-party assistants and AI features in existing software can be enough to justify an assessment.
Will you need confidential prompts or production data?
We agree evidence requirements first and prefer redacted examples or controlled test data where possible.
Does this replace penetration testing?
No. It identifies AI-related risks and control gaps. Any deeper technical testing needs its own agreed scope and authorization.