Employee Assistants
Which internal documents, mailboxes and chat history the assistant can surface, and to whom.
AI Governance, Architecture Review, and Controlled Behavior Testing
Answer the Board's AI Risk Question With Evidence
Your board wants to know what the company's AI can access, do and expose, and nobody in the room can say for certain. Global Digital Forensics establishes that answer with documented evidence: which data each assistant can reach, which actions each agent can take and which controls actually stop misuse. We combine governance review, architecture analysis and controlled testing, and write the findings for executives and engineers alike.
Start from the use case
Security Planned Around How You Actually Use AI
An employee assistant, a model connected to company documents, a public customer application and an agent with access to business tools raise four different sets of questions about data, identity and approval. We begin with how your organization intends to use each system and what it will be allowed to touch, so the work targets the decisions leadership actually has to make: what to approve, what to restrict and what must be tested before launch.
Customer Facing Applications
What a stranger on the internet can extract, steer or trigger through a public chatbot or support agent.
Agents With Business Tools
Which records an agent can change, which payments or emails it can send, and who must approve each action.
Control Whose Authority the AI Acts On
We follow information from the moment it reaches the AI system to the moment it leaves: uploaded files, retrieval indexes, external model providers, connected applications and the stores where prompts and outputs are kept. At each step we check which users can retrieve what and where the system quietly inherits more access than the person using it holds, because that is how a confidential board memo ends up in a junior analyst's answer.
For agents, we review tool permissions, approval steps, credentials and the records of actions taken. The goal is a documented line between a user's authority and what the AI may read or do on that user's behalf, which is the line a regulator or an auditor will eventually ask you to draw.
Not sure where that line sits in your own deployment? Call (800) 868-8189 and walk a senior examiner through the connections.
Methodology
What Happens Between Kickoff and the Board Report
Intended Use: Users, Data and Decisions
We document who uses each system, what it is allowed to touch and which business decisions depend on it.
Authority: Data Flow and Permissions
We trace data and permissions through retrieval, models, tools and vendors to find where the AI holds more reach than its user.
Controlled Testing: Agreed Scenarios
With test accounts and representative data, we try the disclosures and actions that matter, inside stop conditions your engineers set.
Findings: One Report, Two Readers
A decision summary for leadership and reproduction detail for engineering, with a retest plan for each correction.
Governance Review, Architecture Review or Controlled Testing
Governance review
Ownership, approved uses, vendor dependencies, evaluation practice, change management and incident readiness. We write policies that name who approves access, what must be tested before release and which events need human review, so they can be followed on an ordinary Tuesday rather than framed on a wall.
Architecture review
A design level map of data flows, trust boundaries and the controls that belong outside the model. Our AI security architecture review can be scoped on its own.
Controlled testing
Prompt injection, data leakage and tool misuse attempted against the live or staged system under agreed conditions. AI security testing can also be scoped on its own, and AI powered penetration testing is the separate service that uses AI to test your networks and applications.
The NIST AI Risk Management Framework is a voluntary framework many organizations use, and we can organize the engagement around it, around another framework you have adopted or around the business purpose of the system itself.
Advisers Whose Findings Stand Up to Scrutiny
Advice comes from a practice whose examiners have testified in federal and state courts and which has served AmLaw 100 firms and Fortune 1000 companies, led by founder, CEO and CTO Joseph Caruso, a CISSP. Meet the examiners before the first call.
A senior specialist can map this to your systems on a free first call. Schedule the free consultation or call (800) 868-8189.
Records we review
AI Records and Controls We Review
Architecture and data flow documentation shows where the AI receives, processes and shares information. User roles, permissions and connected tools define the authority the system is meant to have. Evaluation records and authorized test results show how controls performed under documented conditions, and where no such records exist, that gap becomes a finding of its own.
What you receive
Your Findings and Action Plan
A system and data flow description, a findings register ranked by consequence and tied to test evidence, an executive explanation connecting each finding to the decisions in front of you, engineering remediation steps and a validation plan for corrections and later model changes. A typical finding might show a support assistant retrieving a document outside the user's permissions, the test that reproduced it, the authorization change to make and the retest confirming the fix held.
Free planning guide
Tell us which systems matter most and we will sequence the testing around them.
Read How We Prove AI Findings
Our nine minute guide explains the failures a sponsor should care about, from indirect prompt injection to retrieval that ignores permission boundaries and agents that call tools they should not, and the evidence that turns a demonstration into a finding engineers can reproduce: logged prompts and tool calls, model and prompt versions, and results across repeated trials.
Download the AI Red Teaming Guide (PDF)
Or read the article online: Reproducible AI Red Teaming for Prompt Injection and Leakage.
Frequently asked
Prelaunch Reviews, AI Agents and Data Exposure
Can you assess an AI system before we deploy it?
Yes. We review the design, data flows, access controls, evaluations and logging, then run controlled testing in an agreed environment, so the deployment team fixes what we find before launch rather than after.
Can you review an AI agent that uses business tools?
Yes. We examine tool permissions, credentials, approval steps, user authority and action logs, and test selected workflows to see whether the agent can be steered into an action nobody authorized.
Can you test whether AI exposes confidential company data?
Yes. Using authorized accounts and representative test material, we evaluate retrieval permissions, access controls and data handling, and show the exact path a document took to reach a user who should not have seen it.
Will executives and engineers receive different levels of detail?
Yes. The executive summary explains decisions and business impact in plain language. The technical findings carry the evidence, affected controls, remediation and validation steps engineers need.
How do we keep the assessment useful after a model changes?
We identify the changes that matter and set out a retest plan covering the affected data, permissions, integrations and behaviors, so your team can build those checkpoints into release and change management.
Give the Board an Answer It Can Defend
Bring the intended use, an architecture or workflow diagram, the data sources, the user roles and the rollout date. On the first call a senior examiner will tell you whether the next step is a governance review, an architecture review or a controlled test, sometimes a smaller engagement than you expected, and will cover the timeline and the price. The consultation is free, and we will arrange a secure way to share architecture documents so nothing sensitive travels by email.
Reviewed by Joseph Caruso, Founder, CEO and CTO, CISSP. Last reviewed .