Artificial intelligence (AI) based cybersecurity tools have evolved in response to this unprecedented challenge to assist information security teams in reducing breach risk and enhancing their security posture.
The attack surface for businesses is enormous and is quickly expanding and evolving. To correctly evaluate risk, up to several hundred billion time-varying signals must be processed, depending on the size of the organization. Analyzing and enhancing cybersecurity posture is no longer a challenge on a human scale.
Identify possible weaknesses
Continuous monitoring, malware prevention, behavioral analytics, harmful bot protection, and attack-surface analysis are just a few examples of the many AI use cases that are crucial tools in the battle against cyber-attacks.
For instance, attack-surface analysis examines systems and procedures to find vulnerabilities. This makes it possible for companies to examine the whole attack surface in the same manner as an attacker. This method enables firms to proactively discover vulnerabilities and ascertain the possible risks associated with data exposure and the possibility of malicious code execution. These insights allow businesses to fix weaknesses and enhance environmental protection.
Recognizing suspicious actions
To identify questionable user activity, Artificial Intelligence (AI) and Machine Learning (ML) are also being deployed. Enterprises employ ML to distinguish between normal behavior and aberrant behavior that might be the sign of a cyber-attack by monitoring the unexpected actions of users, including workers trying to log in at unpredictable times of the day or attempting to make a vast number of downloads. This allows them to address vulnerabilities before a breach happens.
Improve cybersecurity standards
Businesses must maintain consistent cyber-hygiene in order to benefit fully from the AI application. The organization’s cybersecurity posture can be maintained by implementing best practices, including identity and access management, secure coding, vulnerability management, cyber-threat hunting, and constant user education.
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Although AI cannot solve all cybersecurity problems, it does help security teams work more efficiently. While monitoring AI is still necessary, its advantages in fostering creativity, accuracy, and speed are crucial for identifying and reducing cyber-threats.
Evaluating network risk
AI and machine learning are being used by many enterprises to rank the dangers posed by various network parts. In order to decide which areas of the network have been targeted the most frequently, machine learning is being used to examine historical data sets on cyber-threats. Additionally, it is utilized to identify the network components that would cause the most significant harm to the business in the event of a breach. The cyber-analysts are prioritizing their efforts to concentrate on the most significant threats after giving each component of the corporate network a score.
Automate repetitive work
It is difficult for cybersecurity and IT teams to continuously scan for risks since they are focused on daily tasks. AI threat monitoring may lessen the workloads of human employees and free up resources so they can concentrate on higher priority tasks when used in conjunction with other automation solutions. Automation can quarantine and encrypt specific types of files or block suspect traffic based on predetermined criteria, while machine learning algorithms can make more complicated decisions with complete transparency.
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