We are seeing that bias in Machine Learnings Models can be a big issue since the Data available to train these models can be biased. Consequently, using biased Machine Learning Systems can be dangerous when it becomes the basis to make decisions about humans automatically, with no human oversight, resulting in biased outcomes in fields of Employment and Loans. Similarly, another area of concern is ML Models that are being used for Political Reporting with significant “left wing” bias and publishing Reports and Stories with a left leaning slant, which makes the current political divide more pronounced. Putting this
Category: Machine Learning
Strategic Countermeasures to combat Software Vulnerabilities effectively in AI/ML enabled applications
Looking back, Application Security has evolved significantly in the last couple of decades. In the early 2000s, SQL injection and Cross Site Scripting (XSS) attacks were a nightmare for cybersecurity teams as attackers easily bypassed network firewalls through attacks at the application layer. Since traditional network firewalls at that time were not application-aware, these attacks proved a blind spot allowing attackers to compromise web applications easily. Hence, the computer industry developed countermeasures which included and not limited to web application firewalls (WAF), source code security reviews, and DevSecOps, who automate these checks within CI/CD pipelines to and allow security