One of the most significant problems facing application security teams is the amount of time it takes to manage the results returned from automated testing tools. Tests may return thousands of potential vulnerabilities, but most AppSec professionals know that only a small fraction of these are worth the time and effort to remediate. AppSec teams comb through these results and triage them—flagging the ones that should be fixed and weeding out the false positives. This process is extraordinarily time-consuming, repetitive, and tedious—but necessary. Machine learning offers a solution to this problem. This whitepaper discusses the ways that machine learning can be applied to automate the triage process, including solutions already on the market.
- How to use machine learning to increase speed and efficiency of AppSec triage
- ASOC Series Part 1: How Application Security Orchestration and Correlation can improve DevSecOps efficiency
- How to use machine learning to win the time battle against AppSec triage
- Code Dx Unveils Triage Assistant, Takes Aim at False Positives
- How to join DevOps and security: Best practices in DevSecOps
Anita D'Amico application security Application Security Risk Management application vulnerability correlation appsec Attack Surface Detector Award black box testing code exploits Code Pulse Continuous assurance Cybercrime Cyber Defense Magazine Cyber espionage cybersecurity DAST DevOps devops tools DevSecOps DHS dynamic application security testing GitHub GSN Hacker HIPAA human factors hybrid analysis interactive application security testing Jenkins Ken Prole NetSec Newsday NIST open source static analysis tools OWASP Penetration testing risk management RSA SAST software component vulnerabilities static application security testing static source code analysis SWAMP Vulnerability management vulnerability tools