Artificial intelligence (AI) is on the verge of revolutionizing the world. AI offers great prospects, some of which are already being realized and others that will be realized in the future. As AI grows more prevalent in computing applications, so does the requirement for high-grade security at all levels of the system. In the AI process, security must be a priority.
When it comes to cybersecurity, speed is everything. A skilled adversary can sneak into an organization’s network and start exfiltrating important data assets in less than 20 minutes, and as the volume of data produced by modern enterprises grows, it’s becoming increasingly difficult for human analysts to detect malicious activity until it’s too late. This is where artificial intelligence in cybersecurity can come to the rescue.
Because of the hostile threat landscape, businesses are turning to artificial intelligence (AI) as part of their internal and external cybersecurity strategy. Given the complexity of modern threats, human defenders will never be able to keep up, thus enterprises should include artificial intelligence capabilities into their technologies and solutions. AI is now critical for modern organizations to stay up with the fast-changing threat landscape, and it offers a number of use cases that businesses can use to improve their security posture.
Using IR to stop attacks before they start
Incident response (IR) is perhaps the most compelling use case for AI in cybersecurity. AI enables businesses to detect unusual behavior in their settings automatically and respond with automated responses to contain invasions as quickly as feasible.
There will be too much digital data in 2021 and in the future. That is the unadulterated truth. Businesses should use clever AI to detect these attacks; if they do not, there will be a long period of dwell time during which attackers will have free rein.
Charting and labeling protected data
The ability to stay on top of the latest threats isn’t the only compelling use case for AI in cybersecurity. AI also has the capability of automatically processing and categorizing protected data, allowing enterprises to have complete transparency over how they process this information while also ensuring compliance with data privacy requirements in an ever-complex regulatory framework.
Using artificial intelligence and machine learning to comprehend what the data is and to ensure that businesses have a consistent labeling system, as well as to ensure that businesses understand where the data is transiting, a task too enormous for even the most powerful team of security experts.
AI makes it easier for a business to inventory what protected information is traveling by categorizing and identifying sensitive data, so administrators can accurately report to authorities on how that data is handled and prevent exposure to unauthorized individuals.
Building zero-trust architectures
Simultaneously, one of the most unique AI use cases is the ability to design automatic zero-trust systems and ensure that only authorized people and devices have access to privileged information. Artificial intelligence-driven authentication can ensure that only authorized individuals have access to sensitive data.
Implementing so-called zero-trust architectures and continuous or just-in-time authentication of users on the system, as well as device verification, is one of the most powerful new use cases.
Zero-trust AI systems use a variety of data sources to accurately identify and authenticate authorized users at machine speed. Machine-learning algorithms underpin these systems, which employ time, behavior data, location, and other criteria to calculate a risk score, which is then used to permit or restrict access.
When used correctly, these technologies can detect and block unwanted access to sensitive information. Following the widespread shift to remote or hybrid work environments that occurred during the COVID-19 pandemic, these capabilities are becoming increasingly critical.
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