How AI can help with cybersecurity

1. How AI can help with cybersecurity With the digitalization of everything, cybersecurity is becoming increasingly vulnerable. The problem for every IT professional is preventing these cyberattacks and fraudulent activities while saving money on an annual basis. Taking the necessary precautions and detecting such risks are required to prevent these types of cyber threats. That is where AI comes in and simplifies the time-consuming processes of data analysis, data screening, and risk detection. The use of Artificial Intelligence in cybersecurity is growing, and many businesses are incorporating it as a key tool in their cybersecurity strategies. Every day, there are 4,000 new cyberattacks. A ransomware attack occurs every 14 seconds, causing devastating financial losses to companies, while 560,000 new pieces of malware are detected every day. Because of the growing number of cyber threats and the scarcity of skilled cybersecurity professionals, AI in cybersecurity is becoming increasingly important. Continue reading to find out how artificial intelligence can help with cybersecurity. The Use Of AI in Cybersecurity The use of Artificial Intelligence (AI) in cybersecurity has shown great promise in identifying, detecting, and preventing security breaches. ●Known and Unknown Threat Detection: AI-based solutions use machine learning algorithms to detect and respond to known and unknown threats. Traditional antivirus software

2. may miss some known and unknown malware variants, but AI can identify this type of malware behavior and analyze it. AI can assist security teams in monitoring, detecting, preventing, mitigating threats, and managing their workload.Such tools use sophisticated algorithms and predictive intelligence to hunt down malware, run pattern recognition, and detect and thwart attacks that are difficult for humans to detect before they cause harm. They also keep the team updated on new anomalies, attacks, and prevention methods. ●Security log Analysis: Security log analysis is the process of reviewing system-generated event logs to proactively identify bugs, security threats, or other risks. Rule- based systems are limited in their ability to identify new and emerging threats in traditional security log analysis. Machine learning algorithms are used in AI and cybersecurity-based log analysis to analyze large volumes of data in real time. Security log analysis can also be used to ensure regulatory compliance or examine user behaviour. It helps to extract data from logs and find trends and patterns to guide business decisions, investigations, and general security. ●Network security Now, every organization wants to provide services to their customers, and employees expect that they must secure their network security. It helps protectproprietary information from attack. This is why AI algorithms can be trained to detect devices that are not authorized to be on a network, identify suspicious activity, and identify unusual traffic patterns. Anomaly detection can help AI in cybersecurity improve network security. This involves analyzing network traffic to identify anomalous

3. patterns. An AI algorithm can learn what traffic patterns are normal for a particular network and identify anomalous or suspicious traffic based on historical traffic data. It can include traffic from suspicious IP addresses, unusual port usage, or unusual protocol usage. ●Endpoint security Most cybercriminals target endpoints, such as laptops and smartphones. Increasingly malicious and automated cyber activity targets endpoints, leaving organizations that rely on traditional endpoint security approaches vulnerable to attackers who exploit zero-day vulnerabilities and launch ransomware attacks. Traditional antivirus software relies on signature-based detection, which can only detect known malware variants. AI can analyze the behavior of malware and detect unknown malware variants. Endpoint security solutions based on artificial intelligence can detect and prevent unauthorized access to sensitive data. The ability of AI-based endpoint security solutions to adapt and evolve over time is a key advantage. Closing Thoughts In recent years, the increasing use of AI in cybersecurity has presented a transformative opportunity to improve the effectiveness and efficiency of security measures. AI can provide much-needed analysis and threat identification that cybersecurity professionals can use to reduce breach risk and improve their security posture. In security, AI can identify known and unknown threats, perform log analysis, perform network security, and perform endpoint security. Cybersecurity teams can leverage AI to create powerful human-machine partnerships that enrich our lives, push the boundaries of our knowledge, and enhance cybersecurity in a way that seems greater than its parts.