AI Use Cases in Cyber Security
Due to the sheer magnitude of data, the complexity of many information security problems have moved far beyond that of any human-scale problem. AI and machine learning-based tools have emerged for dealing with the most critical problems as they can easily analyze these mass data and produce the insights needed for timely detection of possible attacks.
Global technology giants like Google and IBM recognize the potential of this technology and have used them for addressing many problems. They have proven to be useful in enforcing good cyber practices, rather than trying to chase down malicious activities.
Challenges
AI has been extensively used to solve many different types of problems over the years – some of them were relatively easier while some were notoriously hard. Now many researchers are aiming to harness the power of AI to tackle the threats posed by cybercriminals.
Autonomous threat detection is one such domain. Threat detection, in general, is a fairly complicated and challenging task. More accurate threat detection implies security measures could be taken more efficiently reducing the processing delay and monetary loss.
That being said, tackling cyber threats is no easy job. It presents an array of unique challenges that need to be addressed such as:
- A huge number of devices. Even a medium-sized organization could be employing hundreds of devices including computers, phones, and other IoT devices that need to be actively connected to the internet.
- A vast attack surface.
- The deficit of skilled security professionals. This is maybe the biggest challenge for any organization. No matter how good or accurate the solution is, there is always some scope of error due to human errors.
- Mass data.
Use cases of AI
Intelligent Posture Management could be an effective way of handling these problems. Posture management plays an important role in cloud security. Posture Management tools can effectively handle an array of tasks including compliance monitoring, incident response, and risk assessment.
Such a system could gather data from across your enterprise information systems. This data could be used for analyzing and gathering useful intel. Some of the use cases are as follows:
Asset inventory
Properly categorizing and measuring the business criticality plays a huge role in inventory management. It could be useful for gaining a complete view of the current status of the inventory (say the number of active devices, users, or applications).
Threat exposure
Like everything else, most of the attacks also have certain patterns – they follow a trend. And this may change over time – what is true today may not be true tomorrow anymore. AI-powered tools can help provide the latest insights about industry-specific threats. This also helps in making informed critical decisions and set priorities.
Breach risk prediction
Various AI techniques have been widely used for predicting the most likely place of a breach. This not only gives you an estimate of where the next breach is most likely to take place but also how likely it is and when it might take place. These insights can be used for improving the cyber resilience of your organization by allocating resources in those weak areas.
Other useful articles:
- AI Use Cases in Cyber Security
- Machine Learning Helps Pick Better Passwords
- Microsoft’s New Framework Can Protect ML Models
- Microsoft’s New Model Detects Password Spray Attacks
- People Overestimating The IoT Devices
- The Kimsuky Module Is a New Threat