Aisy is emerging from stealth with a $2.3M seed investment from 6 Degrees Capital, Flying Fish VC, Osney Capital with angel investors and advisors from DeepMind, Cisco, HP, and Trail of Bits. Aisy is building the AI-native platform that transforms vulnerability management from reactive ticket triage to proactive, threat-focused risk reduction.
AI is changing the game for attackers
Artificial intelligence is fundamentally reshaping the threat landscape. Attackers now leverage AI to automate reconnaissance, identify vulnerabilities at machine speed, and orchestrate sophisticated attacks across thousands of targets simultaneously. What once required weeks of manual effort can now be executed in hours. The pace and scale of attacks are accelerating beyond what traditional security frameworks were designed to handle.
Meanwhile, defenders remain trapped in workflows built for a slower, more predictable era. Existing vulnerability management tools assume humans can manually review, contextualize, and prioritize threats. This assumption has become untenable.
The asymmetry is stark: attackers use AI to amplify their capabilities while defenders remain burdened by legacy processes that generate overwhelming backlogs without providing actionable intelligence. We need tools that match the sophistication and speed of modern threats.
Context is the scarce resource
Enterprise security teams manage backlogs of hundreds of thousands, sometimes millions, of vulnerability reports.
The problem is not insufficient data. The problem is insufficient context. Without understanding how vulnerabilities relate to business threats, how they chain together in attack paths, and which fixes address root causes, even sophisticated security teams cannot effectively prioritise risk.
Traditional vulnerability management tools ask the wrong question: “Which of these 100,000 tickets should we fix?” rather than rethinking what belongs in the backlog.
The market is saturated with scanners and ticketing systems. What enterprises lack is context: infrastructure topology, external attack surface, business-critical assets, and how vulnerabilities connect to form exploitable chains. This context cannot exist in a spreadsheet.
AI-native architecture
Aisy represents a fundamental shift in how security tooling is built. Rather than bolting AI capabilities onto existing vulnerability management infrastructure, Aisy is AI-native from the ground up.
Aisy inverts the security model. Rather than starting with vulnerability scan outputs and attempting to rank them, Aisy begins with the threats that keep CISOs awake at night: user account takeover, service disruption, data exfiltration. From there, the platform connects the dots between vulnerabilities, infrastructure, and attack chains to surface what genuinely matters.
This attacker-driven methodology reflects years of real-world exploitation experience. They understand how vulnerabilities link together, which misconfigurations enable lateral movement, and where a single strategic fix can render thousands of potential exploits irrelevant.
Aisy codifies this reasoning by mapping environments from the outside first, using the same reconnaissance techniques employed by bug bounty hunters to discover assets, routes, and services.
Founder
Shlomie Liberow, Founder and CEO of Aisy, brings a rare combination of offensive and defensive security expertise. He spent seven years at HackerOne, where he was Head of Hacker R&D. In this role, he worked alongside some of the world’s most skilled hackers, gaining deep insight into real attacker behavior and the techniques that consistently bypass legacy defenses.
This background provides him with an unusual vantage point: he understands both how attackers think and why conventional vulnerability management traps security teams in endless reactive cycles. Aisy was built to solve this problem by bringing attacker-grade intelligence to defenders.
Why Aisy is different
Aisy is trained on proven bug bounty logic, incorporating the techniques researchers use to identify complex, chained vulnerabilities that automated tools routinely overlook. This approach yields distinct advantages.
First, Aisy identifies root causes rather than symptoms. By understanding attack chains, the platform can surface fixes that eliminate whole groups of vulnerabilities simultaneously, rather than forcing teams to remediate thousands of individual reports one by one.
Second, Aisy continuously contextualizes output from scanning tools through external mapping and threat modeling, ensuring that prioritization remains aligned with evolving business threats.
Third, the platform operates as a force multiplier for security teams, augmenting their capabilities rather than adding to their workload.
We believe Aisy can become the category leader and system-of-record for outcome-driven exposure management. As the platform embeds into enterprise workflows and learns from remediation outcomes, it will compound its advantage through network effects.
The broader trend toward AI-native security tools is inevitable as defenders need systems that can process and contextualize information at the scale and speed that modern attack surfaces demand. Security teams that adopt Aisy will fundamentally shift from reactive triage to proactive risk reduction.
