Use cases
How security teams
use Modelsight.
11 patterns. The questions Modelsight answers, the scenarios it supports.
01
Org-wide AI visibility
Initial AI inventory & risk assessment
We know employees use AI, but we don’t know which tools, how much, or for what.
How it helps
- Discover AI API usage via proxy and CASB/Microsoft connectors
- Map adoption by user, team, and volume
- Build model inventory with sanctioned/unsanctioned status
Outcomes
- Visibility teams didn’t have before
- Foundation for AI policy decisions
02
Shadow AI
Unapproved AI tool identification
We approved ChatGPT Enterprise and Copilot — but data is going to other AI services.
How it helps
- Detect new AI tool adoption as it happens
- Real-time inventory of unsanctioned services
- Decision support: sanction, block, or monitor
Outcomes
- Catch shadow AI before it entrenches
- Reduce unknown risk surface
03
Data exfiltration
Sensitive data sharing detection
An engineer is pasting production database queries into ChatGPT to debug.
How it helps
- Request bodies captured via proxy for review
- DLP-style rules flag sensitive-data patterns
- Attribute flagged events to specific users
Outcomes
- Early detection prevents exposure
- Reduced breach and compliance risk
04
Departing employees
Departing-employee monitoring
An employee gave notice. We want to ensure they’re not exfiltrating data.
How it helps
- Review recent AI usage history per user
- Flag elevated request volume for review
- Forensic record for legal or HR follow-up
Outcomes
- Early warning before departure
- Evidence on hand if needed
05
Compromised accounts
Compromised-account detection
Credentials were phished. The attacker is using the account to extract data via AI.
How it helps
- Surface unusual model access, frequency, or off-hours activity
- Flag suspicious events into alerts
- Audit log of every platform action
Outcomes
- Faster detection of compromise
- Reduced attacker dwell time
06
Regulated industries
Healthcare, finance, government
We’re regulated and need to prove AI controls for our next audit.
How it helps
- Comprehensive audit trail of AI usage
- Documentation of where regulated data goes
- Detect policy breaches before auditors do
Outcomes
- Pass audits with evidence
- Avoid AI-blind-spot findings
07
SOC 2 & ISO 27001
Control evidence for the auditor
Auditors ask how we monitor AI tools that access customer data.
How it helps
- Continuous monitoring (CC7.2, CC7.3)
- Incident detection capability evidence
- Access oversight reports (CC6.1)
Outcomes
- Pass with AI controls in place
- Win customer trust on review
08
Incident response
Post-incident forensics
We detected a breach. Initial signals say AI tools were used to exfiltrate.
How it helps
- Filter AI events to the incident window
- Access captured request/response bodies
- Export evidence for legal or HR review
Outcomes
- Faster investigation, full telemetry
- Clear picture of what was affected
09
Threat hunting
Proactive search for AI-driven activity
We’re hunting for compromise or insider activity. AI usage is an indicator.
How it helps
- Search events by user, model, provider, time
- Review prior flagged events and alert history
- Export telemetry for SIEM correlation
Outcomes
- Catch threats traditional tools miss
- Better TTP intelligence
10
Policy development
AI policy development & enforcement
We’re writing an AI AUP — but we don’t want rules that ignore how people work.
How it helps
- See how employees actually use AI before writing policy
- Discover why users pick shadow AI
- Monitor adherence after rollout
Outcomes
- Policy grounded in reality
- Higher voluntary compliance
11
Tool approval
Approve new AI tools with evidence
Teams are requesting new AI tools. We need a way to assess risk.
How it helps
- Understand actual usage patterns of requested tools
- Track usage during pilot or trial periods
- Ensure approved tools are used as intended
Outcomes
- Data-driven approval decisions
- Balance security with productivity
Map ModelSight to your use cases.
30-minute walkthrough tailored to your environment.