All machine learning models come with risks including a large segment of false positives when learning algorithms are bad. Sometimes hackers can exploit them. Contaminated or compromised data from a recently hacked host could also cause serious damage to the effectiveness of the platforms predictions. Hackers can misuse biometric authentication methods to gain entry and play havoc, just as in any other system. They can also fool the system into classifying defective training samples as genuine and reaching predictions based on them. This will cause the model to show a wide variation from the expected feature select outputs.

Use of ethical hacking The services of an ethical hacker who simulates break-ins to discover vulnerabilities overlooked by the firewall, intrusion detection system or other security tools.

 

 Security logs encryption The system administrators and other users should follow a strict laid down security protocol to avoid accidentally letting in mischievous elements. One of the basic necessities is to always use encryption software for data handling.

 

DevOps for model lifecycle False positives make machine learning platform vulnerable. DevOps in the learning model lifecycle helps prevent this vulnerability. DevOps begins with the development and training phase, through quality assurance.

 

Implement strict security policy A security protocol must be implemented from the beginning as an organic part of the machine learning risk management.