Prem Cyberscan: How Enterprises Use AI Security Agents to Find Critical Code Vulnerabilities

Prem Cyberscan is an AI-assisted continuous code-review tool designed to supplement, rather than replace, periodic security audits, human review, penetration testing, and specialized assessments. It addresses the gap created when code, dependencies, configurations, and features change between formal audits, using the Coldcard wallet incident as an example of how a long-standing regression can lead to major consequences. Connected through GitHub, Cyberscan reviews repositories in system-wide context, analyzes potential vulnerabilities and attack paths, produces severity-ranked findings with file and line references, and integrates results into GitHub code scanning, APIs, and MCP-compatible tooling. It uses selectable open-weight models including Kimi K3, a DeepSeek-based model, and Qwen, with scans processed through Prem’s EU North infrastructure and isolated workers using clean checkouts for each run. The service charges by token use, retains scan metadata and reports while noting that automatic expiry and independently verifiable cleanup are not yet available, and encourages teams to validate findings because AI analysis can produce false positives and miss context-dependent flaws. Available in beta with free introductory credits, it is aimed at engineering, AppSec, infrastructure, and security-sensitive teams seeking ongoing monitoring of fast-changing or large codebases.

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