How a Global Tech Enterprise Ran an AI-Native Security Audit On Its Own Infrastructure
A global network infrastructure leader was swamped with cybersecurity alerts. They wanted to use an advanced AI-native security system to pinpoint real vulnerabilities, but they couldn't send their source code to a third-party vendor. To get frontier-class AI performance within the perimeter they already trusted, the company ran a one-time AI-native security audit: AISLE Snapshot. Snapshot discovered dozens of high-impact issues while filtering out over 50% of the potential findings due to reachability issues.
Key Results
89 prioritized findings delivered in days along with exploit narratives & step-by-step data-flow traces
50% of potential findings automatically filtered out due to lack of reachability
1.5M+ LOC analyzed by AI without sharing source code
Multiple issue classes surfaced that existing tools can’t catch
A global network infrastructure provider was struggling to manage vulnerabilities across their massive codebase. Traditional SAST tools buried the team in false positives while missing genuine risks. "The amount of time and work that it takes to find, analyze, fix and just generally manage vulnerabilities, combined with all the different tooling you need across distinct program languages, is overwhelming,” said a senior member of the product security organization.
When cybersecurity leaders heard about AISLE’s AI-native approach to vulnerability management, they were excited to try it. However, internal discussions led them to conclude that AI security tooling was off the table. After all, with so much sensitive IP at stake, they couldn’t risk sending source code to a third-party vendor.
Then the team learned that AISLE Snapshot delivers frontier-class vulnerability discovery in any deployment environment, from cloud to air-gapped. With the help of two AISLE forward-deployed engineers (FDEs), they ran Snapshot within their own perimeter to find dozens of high-value findings. As a product security engineer said, “Snapshot doesn’t just find issues, it investigates them.”
Running AI-Native Security Analysis While Protecting Sensitive IP With AISLE
Like most security organizations, defenders at the enterprise were extremely pressed for time. "We have limited resources, a lot of products, and about 1 million lines of code per product," they said. Traditional scanners worked without sending enterprise IP to vendors, but they generated hundreds of potential vulnerabilities across just a few repositories, making it difficult to prioritize what truly mattered.
Making matters worse, these traditional scanners missed complex vulnerabilities, particularly server-side request forgery (SSRF) issues and zero-day vulnerabilities that combined multiple issues. The team needed a solution that could complete the long reasoning chains to find these zero-days and reduce the security burden on developers. It asked AISLE to send FDEs and run Snapshot within its existing perimeter as a way to find real vulnerabilities and weed out false positives.
Pinpointing Genuine Risks Using Snapshot
AISLE’s FDEs delivered Snapshot as a downloadable bundle with no external plugins. They then ran it through the network infrastructure provider’s existing Amazon Bedrock account, with no code or findings sent to AISLE.
After analyzing two repositories (Go and a legacy codebase) with about 1.5 million lines of code, Snapshot delivered a list of prioritized findings with full exploit narratives. The customer’s team turned the findings into about 30 reports, with four issues completely remediated and six more in review in the first 20 days.
One issue stood out: it demonstrated a concrete exploit with behavior so unexpected that it became a training asset for developers, helping them become more security-aware. The product security team summed up its experience in the following table:
Rethinking Product Security With AISLE
By delivering AI-native analysis without external plugins, AISLE opened new possibilities for the customer’s product security team. As they concluded: “AISLE finds vulnerability classes that our existing tooling misses, particularly issues requiring data-flow tracing from input to dangerous sink.”
Strict data sovereignty requirements are no longer an obstacle to obtaining best-in-class security analysis. Get Your Snapshot
