Cybersecurity threats are increasing at an unprecedented rate, and there is numerous instance where organizations are being breached. For this very reason solution providers are moving away from legacy structures of offering security solutions and are innovating themselves in the best way possible. Microsoft, Amazon.com Inc., Google, and various startups are moving away from exclusively using older technology designed to respond to only a particular kind of intrusion and instead of innovating themselves.
The tech giants are deploying machine-learning algorithms that condense vast amounts of data on logins, behavior and previous attacks to filter out and stop hackers. One can say that machine learning is an immensely powerful technique for security owing to the reason it is dynamic, whereas rules-based systems are extremely rigid as this is a manually intensive process to change them, whereas machine learning is automated, dynamic and one can retrain the information quickly.
However, it is not right to underestimate the capabilities of the hackers as they can also harness the power of machine learning to create innovative mischief and surpass the new defenses. For example, they are now capable of figuring out how organizations train their systems and use the data to dodge or corrupt the algorithms. The big cloud services organizations are well aware that cybercriminals are innovating themselves with each passing day but as technology evolves there are slight chances that the criminals can be over-powered. According to experts, there are higher chances to see an enhanced ability to identify threats earlier in the attack cycle resulting in a reduced total amount of damage and speedier restoration of systems to a desirable state. They also recognize that it is impracticable to stop all intrusions, but there are ways to incrementally amend at protecting systems and make it progressively difficult for attackers.
To do a genuine job of estimating out who is admissible and who is not, Microsoft technology has started a program where they can learn from the data of each firm using it and customizing security to that client’s typical online behavior and past. Since introducing the service, the company has managed to reduce the false positive rate to .001 percent.
Google also now checks for security breaches even after a user has logged in, which comes in handy to capture hackers who at the start look like real users. With machine learning able to analyze numerous pieces of data, grabbing unapproved logins is no sustained a circumstance of a single yes or no. Instead, Google monitors various aspects of behavior throughout a user’s session. Someone who looks legit at the start may later display symptoms they are not who they say they are, letting Google’s software boot them out with adequate time to avert further damage. Apart from using machine learning to guarantee their networks and cloud services, Amazon and Microsoft are providing the technology to customers. Amazon’s Macie service utilizes machine learning to find sensitive data amid corporate info from customers like Netflix and then watches who is entering it and when alerting the company to irregular activity.