INFERA AI.SafeCode addresses the growing crisis in Application Security where traditional integration of separate SAST, DAST, and SCA tools produces hundreds of critical alerts, up to 80 percent of which are false positives.
The rapid adoption of AI coding assistants such as GitHub Copilot, Cursor, and Claude has multiplied both development speed and the volume of insecure code, turning AppSec into a bottleneck when legacy processes remain unchanged.
INFERA AI.SafeCode unites seven specialized engines under a single DevSecOps and MLSecOps framework: real-time SAST in IDE and CI/CD, SCA for open-source components and transitive dependencies, Secrets scanning with validity verification, DAST for running applications, an AI Pentest agent that simulates manual testing in a sandbox, Code Fuzzing for crash vulnerabilities, and API Fuzzing covering REST, GraphQL, gRPC, and WebSocket interfaces.
Results are normalized and deduplicated into a unified risk registry that automatically assigns high and critical issues to developers together with explanations, proof-of-exploit evidence, and remediation guidance.
Cross-validation between engines replaces manual triage. When SAST identifies a potential SQL injection, the platform automatically builds a targeted fuzzing harness; Code Fuzzing then attempts exploitation and returns a conclusive stack trace only if a crash occurs. Similarly, DAST detects authorization flaws such as IDOR by comparing responses across roles, after which the AI Pentest agent constructs attack graphs up to twelve steps deep to determine whether the issue can escalate to remote code execution.
Security checks run continuously inside the IDE, highlighting problems before commit and offering concrete AutoFix patches. Integration with GitLab and GitHub posts comments and ready pull requests directly on affected lines, while vibe-coded AI output is validated to prevent hallucinated vulnerabilities from entering the codebase.
API Fuzzing exercises stateful scenarios including authentication flows, mass assignment, JWT bypass, and malformed payloads using specifications such as OpenAPI, GraphQL schema, and .proto files. SCA analysis links vulnerable dependencies to actual code paths, distinguishing reachable CVEs from unused libraries, which is especially critical for organizations subject to Russian critical infrastructure regulations.
Secrets detection distinguishes test tokens from valid production credentials by performing live verification, converting potential noise into immediate incidents that trigger key revocation and investigation.
A centralized project view aggregates repositories, scan histories, attack-surface data, and metrics such as MTTR and SLA violations. The attack-surface module visualizes subdomains, open ports, endpoints, and entry-to-sink chains, giving CISO, AppSec, and development teams a shared, real-time picture of organizational risk.
Self-hosted deployment supports strict enterprise access controls and allows import of internal vulnerability databases to meet requirements of FSTEC Order No. 117, Order No. 21, GOST R 56939-2024, and Federal Law No. 152.
By confirming vulnerabilities before blocking pipelines and by providing reproducible evidence rather than raw alerts, INFERA AI.SafeCode enables development velocity while preventing uncontrolled growth of technical and security debt.
