Above Security is the inaugural sponsor of the Insider Threat Matrix
Read more
Use case
Once customer data lands in a personal ChatGPT, Claude, or Gemini account, it's outside your perimeter — invisible, unrecallable, ungovernable. Above sees the moment it happens and tells you exactly what crossed the line.
Capablanca solved positions with the minimum force needed. Surgical, economical, no wasted moves. Shadow AI rewards a scalpel — not a sledgehammer.
Name the customer, the data, and the AI account — by evidence.
Replace blanket bans with targeted controls that don't punish the people doing it right.
Brief your customers with specifics — not vague reassurances — when something actually crossed the line.
You manage security at a knowledge-work company where ChatGPT, Claude, and Gemini have already arrived. Most of your people use them exactly the way you'd hope. A few don't — they paste live customer call transcripts, named-account pricing, and bulk PII into personal AI tabs signed in to an @gmail.com identity. You suspect this is happening at scale. You can't yet name the customers, the employees, or the platforms. Does that sound like your organization?
Your team is offered a binary choice nobody wants: ban the tools and frustrate the workforce, or allow them and accept unquantified exposure. Neither is acceptable. Data that reaches a personal AI account doesn't come back — your team can't see what was sent, audit it, or recall it. The breach happens before the rule is written.
01
Above's investigative agents observe the activity, verify the AI account by evidence, and name the customer whose data crossed the line — so your security team responds to a fact pattern, not a guess.
02
With the prompts in hand you can choose between a corporate AI seat, a per-team policy, a per-platform block, or a quiet one-on-one — whichever fits the user, the team, and the data — so a high-performing AE isn't treated the same way as a repeat offender.
03
When data has crossed the boundary, the investigation gives you the named customers, the dates, the platforms, and the volume — so your account owners brief their customers with specifics and your CISO walks into the board meeting with a finished story, not a forming one.
Outside your perimeter, into a data lake you don't control. Personal ChatGPT, Claude, and Gemini accounts function as unmanaged storage for whatever the user pastes — and once a customer transcript, deal note, or named-account pricing lands there, your security team has no visibility into what was sent, no audit log to pull, no retention policy to apply, and no recall. The data has crossed from a place you can govern into a place you cannot. For most insider-risk activity involving personal AI, the boundary crossing is the entire event — everything after is consequence management.
DLP fires on data movement against predefined policies — pasting into a tab on the same authenticated corporate device is not the data-movement event the policy was written for. UEBA fires on deviation from a behavioral baseline — but pasting into a generative-AI tab quietly became the baseline the moment those tools arrived. Both categories share the assumption that insider risk can be predefined as a rule or a deviation. Personal-AI exfiltration is neither. It is a behavioral-context question — what was pasted, by whom, into what kind of account, fitting what pattern over time — and answering it requires continuous behavioral investigation, not another policy.
Above's investigative agents reconstruct the behavioral timeline: the device the paste came from, the AI account it landed in (corporate or personal, verified by evidence not assumption), the customer or content category exposed, whether this is a one-off or part of a recurring pattern, and the surrounding behavioral context — role change, departure signals, prior conversations, peer activity. The output is not a single alert that says “block this user.” It is a structured investigation — timeline, contextual analysis, reasoning, recommended actions — that security, HR, and legal can act on together, with the response calibrated to the pattern rather than the personality.
