The idea of using AI to combat cybersecurity breaches is nothing new. However, it is important to remember that cybersecurity relies on the technical skills of those who implement and design the technology, which means that experts have virtually no room for error. It's a daunting task. Thousands of lines of code must be written and then verified to ensure that the software used by companies to protect their network is free of vulnerabilities. For total control, companies must be able to learn from their past mistakes and learn how to leverage existing data to increase security.
Using AI to Secure Your Network
With machine learning, a key component of AI, companies can develop technologies that can detect and single out malicious software. This is possible in part because of the vast amounts of data on corporate networks that can be used by machines for analysis and learning. With this visibility and resources, IT professionals can make significant strides in their quest for an AI that can autonomously and effectively protect large, connected networks.
These large networks will continue to expand with the current rise of the Internet of Things (IoT). The IoT poses a significant threat, as highlighted in a recent Aruba Networks study that examines the way this technology is most susceptible to security breaches than traditional hardware. By 2019, up to 89 percent of healthcare institutions will have adopted some form of IoT. Yet 89% of companies that have already deployed the technology have experienced a security breach. The study also shows that government agencies (85%) have experienced a large number of security breaches. Fortunately, solutions are currently available or under development to mitigate these risks.
Intensive-based networking, or intuitive networking, for example, is a concept that is already operational. It is software that helps plan, design, implement and operate a network. It translates corporate goals and policies into a network configuration and then validates the resulting design and configuration. The key for AI is to be able to feed on readable and actionable data, which means that networks with high traffic can make decisions better based on user behavior. The UEBA (User and Entity Behavior Analytics) technology has been designed precisely for this purpose. Using machine learning, it checks which endpoints are using the network and for what purpose.
On the risk mitigation side, the knowledge gained through data processing and analysis allows networks to be programmed to automatically perform certain actions in response to malicious behavior. Suspicious Individuals are prompted to re-authenticate and obvious dangers are quarantined. In the event of a security breach, the AI can use its knowledge to assign a risk score to the threat, helping the security team quickly detect and counter advanced cyber attacks.
Used together on corporate networks, artificial intelligence and machine learning enable the different forms of risk associated with cyberattacks to be understood and responded to effectively. Data is a key part of the process, but diligence is just as important, given that cybercriminals are usually one step ahead. Nevertheless, with advances in technology and the volumes of data that move through networks, AI has much to learn to ensure the security of organizations, especially in sensitive fields like AI in finance.
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