Aiseptor provides a network-layer security platform that blocks AI-assisted cheating in high-stakes online assessments by preventing invisible overlays, remote-access tools, and on-device LLMs from operating during an exam. The platform deploys as an ephemeral security enclave on any Windows or macOS device in under 60 seconds, with no kernel drivers or persistent installation, and has blocked over 25 attack vectors across signed pilots with assessment platforms.
- Artificial Intelligence
- Cybersecurity
- Software Only
Funding
Founders
Product
Problem
AI-assisted cheating in online assessments has escalated from an edge case to a structural crisis, with fraud attempt rates on proctored assessments more than doubling from 16% in 2024 to 35% in 2025)SkipTheLine. Existing browser-based proctoring solutions fail because modern cheating tools operate below the visibility threshold of every current sensor, using invisible overlays, remote-access tools, and on-device LLMs that bypass webcam and keystroke monitoring entirely.
Solution
Aiseptor is a network-layer security platform that stops AI cheating at the device and network layer rather than the browser, intercepting the pathways that modern cheating tools rely on. The platform deploys as an ephemeral security enclave on any Windows or macOS laptop in under 60 seconds, with no kernel drivers and no persistent install, and disappears cleanly when the exam ends. When a candidate launches a screen-capture tool or other prohibited application mid-exam, Aiseptor blocks the path, flips the session posture from clean to incident, and provides the proctor with the process name, the candidate, and a remediation step—without requiring footage or behavioral guesswork. The system blocks 25+ attack vectors including invisible overlays, remote-access tools, and on-device LLMs, and is currently in production with signed pilots for assessment platforms and enterprise hiring teams.
Target Audience
Primary customers are assessment platforms, certification bodies, and enterprise hiring teams that conduct high-stakes online exams and need to block AI-assisted cheating at the device and network layer rather than relying on browser-based proctoring.
Features
- Network-layer attack interception that blocks invisible overlays, remote-access tools, and on-device LLMs before they can access exam content
- Ephemeral security enclave deployment in under 60 seconds on any Windows or macOS device, with no kernel drivers and no persistent installation
- Real-time posture flipping that transitions a session from clean to incident the moment a prohibited tool launches, capturing the process name and candidate identity
- No webcam or keystroke dependency, eliminating behavioral guesswork and false positives from detection-only approaches
- Public bug-bounty program that invites the security community to stress-test the architecture, with over $300K in bug bounties earned by the founder across Fortune-500 programs