Machine Learning Handles App Risks – Humans Asked to Step Aside!
Is the time of the Terminators and Skynet now upon us? The thought of relying on automata may be unnerving, but machines and their blazingly fast intelligence are now mandatory to achieve the speed, efficacy, and predictability needed to battle against app risks and threats.
Machines are the only way to analyze mobile apps at scale (potentially millions) and at speed (within minutes.) Learning algorithms that allow machines to identify patterns or spot binary code in common with known bad actors, like AppVisualizer does, can operate orders of magnitude faster than any human being. This binary analysis is also critically important because it avoids the risk of malware reacting or morphing under test.
At this stage, the best contribution human beings can make is to step aside and let Machine Learning do its work. In Mi3 Security applications of machine learning, both detection and prediction of risks and threats are offered. New malware variants will trigger alerts, as the learning algorithms lead to predictions based on past experience.
Analysis and scoring allows each app to be compared with threshold levels for security and privacy risk to determine if an app is to be accepted or not. The verdict is simple and effective: an app is either “In or Out.”
The results of machine learning can also be combined with other systems, such as EMM (enterprise mobile management) applications. Mi3 Security AppInterrogator works with these programs to pinpoint risks to an organization from both in-house and third party apps. With a multi-layered policy engine that combines pre-configured templates with user-defined thresholds and policies, the entire app inventory of an enterprise can be seamlessly, rapidly, and frequently vetted.
Nonetheless, human beings still have a major and critical role to play. They must ensure that steps are taken to improve security afterwards. Besides declaring an app “In or Out,” AppInterrogator gives users access to in-depth app risk reports. Inside these reports, detailed information and key insights from the binary analysis of each mobile app lets IT and security teams plan more effective safeguards against threats.
The future of human-machine relationships has been described as cooperative, rather than competitive. Handling app risks and threats is an example of that. Machine learning generates insights that humans might have missed and providing far broader and more complete analysis. Humans stay ahead of machines in terms of business acumen, faculties of judgment, and innovation.
As a result, machine learning and human judgment together make security postures more robust. Risk and threat detection keeps pace with ever increasing numbers of apps, whether new or upgrades of previous versions. Predictive capability allows new threats to be knocked out in advance. At the end of the day, enterprises and organizations, and their users, are better equipped to stay safe, which is what really counts.
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