Utilizing AI for proactive threat hunting and incident response.

Guest post by Harikrishna Kundariya, and opinions are those of the guest blogger.

Introduction

Artificial intelligence (AI) is increasingly used to detect and cease cyber security risks and threats, and if this is not possible, precautionary actions should be taken before the threat is triggered. AI cyber security solutions review and analyze the code used in the virus and develop and apply an antivirus solution. The two codes are stored on databases of previous threats and attacks. The detection and preventive actions used, as well as analyses of new and covert threats camouflaged as innocuous payloads, and actions before the deadly payload is released.

Available cyber security software applications use algorithms and codes to detect malware and viruses with specific destructive code patterns. This detection is not guaranteed and new variants of viruses and hacking exploits are missed. They enter the IT system and hijack or destroy it. A report from Cybercrime Magazine indicates that the costs associated with cybercrime are projected to rise to $19.5 trillion each year by 2025.

How AI Assists in Identifying Threats and Swift Incident Response

AI agents, also called AI bots, are small software programs, with special tasks and routines embedded in either/both, software and/or hardware components. They are programmed to identify vulnerabilities in software programs that hackers tend to exploit. Malware is also found in emails and attachments, illegal sites such as adult and gambling, gaming sites and apps, and others.

These agents also guard and listen to access points’ login areas and virtual ports where software programs connect to the hardware. They also study connection requests from users and/or other software programs, analyze fake or harmful connection requests sent by hackers, and block them.

The AI agent identifies the illegal traffic from its AI learning and training database and stops the request. The request is further analyzed and stored in the training database, which serves as a knowledge repository. This information is shared with other verified cybersecurity agencies.

Using AI for Protection

Many cyber security actions detailed in this section are already available in anti-virus solutions. However, AI systems carry out the detection faster and the response is more accurate.

AI assists in threat detection using a variety of methods, including:

Identifying Threats:

AI systems are refined with machine learning frameworks where they are trained with large examples of exploits and vulnerabilities. The AI bots constantly analyze incoming and outgoing traffic in the server of a business or internet service provided. When a threat or attack is detected, the AI system checks the stored threat samples in the database and blocks the threat accordingly. New variants that are different in structure from the stored examples can be launched.

Faster Incident Response:

With automated and fast incident response, the AI system, which runs on fast computers and servers, analyzes the incident. Threat severity, possible damage, and source of threats are assessed, and actions are taken. Since AI systems constantly learn, they refine response strategies and reduce the time taken to respond. As a result, attacks are blocked before they enter the system. Cyber security managers can later manually review the threats and responses and decide if the actions were correct.

Accurate Prediction:

With deep learning and neural networks, AI systems offer predictive analysis of threats. The AI systems analyzes data traffic and patterns, finds when large resources used for attacks are gathered and predicts attacks. These systems use analytics to detect anomalies, study complex data, evaluate the relation between disparate and unrelated events, and predict attacks fairly accurately.

Adversarial Defense:

The new type of cyber attack, called Adversarial Attacks, occurs when AI systems are ‘poisoned’ or manipulated by malicious data. As a result, AI systems are trained to believe that hacking attacks and malware are legitimate. New generative adversarial networks are developed and trained to identify such spurious data, identify and counter new and unknown attack vectors, and respond appropriately.

User Behavior Analysis:

Legitimate users typically exhibit certain behaviors, such as clicking on links, browsing, searching for information, and downloading files. In contrast, attackers display different and often unusual behaviors. These can include attempts to locate the root folder, searching for server IP addresses, scanning ports, or uploading suspicious files. Their IP addresses often originate from banned or questionable locations, and their contact information is likely to be false. AI systems monitor these behaviors and identify anomalies in real time.

Malware and Fraud Detection:

AI systems can quickly detect phishing, impersonation, email-borne viruses, Trojans, and other attacks. AI systems trace the source of these attacks, compare the IDs with the database, and take action. Emails with suspicious attachments and carrying viruses are deleted or quarantined. Phishing emails and banned sites that inject viruses into the visitor’s browser when people visit certain sites are detected and placed on a blacklist. All traffic from such sites is blocked.

Conclusion

The discussions show that cyber security threats are increasing in intensity and complexity. These threats cost businesses trillions of dollars. The use of AI systems increases the reliability and accuracy of cybersecurity efforts. The systems not only provide security but also aid in preventing losses from attacks and data theft.

AI presents many advantages, such as enhanced and accurate threat detection, faster incident response, accurate prediction, adversarial defense mechanisms, analysis of suspicious behavior, and malware and fraud detection. With the increase in cyber-attacks, AI is proving to be an effective means of preventing such attacks.

Biography: Harikrishna Kundariya is a marketer, developer, IoT, Cloud & AWS savvy, co-founder, and director of eSparkBiz Technologies. His 12+ years of experience enables him to provide digital solutions to new start-ups based on IoT and SaaS applications.