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AI-driven Approaches for Detecting Cyber Threats

AI and ML June 8, 2023

Role of AI in Cyber Security

Cyber dangers pose a substantial risk to individuals, businesses, and governments in the connected world of today. The continual innovation of cybercriminals' strategies makes it difficult to identify and eliminate possible risks.

As businesses gear up for digital transformation, cyber threat detection systems powered by AI have become effective tools for detecting and thwarting online threats. AI can improve cybersecurity measures and aid in the protection of digital assets by utilizing advanced analytics and machine learning algorithms. This blog will discuss AI's function in detecting cyberthreats and explain how it helps us improve our defenses against cyberattacks.

Benefits of AI in Cyber Threat Detection

Let us take a look at some of the major benefits of employing AI to identify and counteract cyber threats.

Accelerated Threat Detection

AI-powered systems are excellent at quickly evaluating huge amounts of data in order to quickly identify cyberthreats. AI can swiftly spot abnormalities and patterns suggestive of malicious activity by continuously analyzing network traffic, system records, and user behavior. Organizations are able to react quickly thanks to this rapid threat detection, reducing the potential harm brought on by cyberattacks.

Force Multiplier in Containment and Response

By automating different processes for containment and response to cyber attacks, AI increases the effectiveness of cybersecurity teams. AI-powered systems are capable of generating alarms, starting incident response processes, and even putting in place countermeasures to mitigate the threat automatically. This automation greatly shortens response times, minimizes human error, and makes sure that cyber incident response is uniform and well-coordinated.

Proactive Security Posture

Traditional cybersecurity techniques frequently use pre-defined criteria or known attack signatures to find risks. But since hackers' strategies are always changing, rule-based systems have a hard time keeping up. Threat detection systems powered by AI are capable of proactive adaptation and learning from new threats. AI can detect new and undiscovered dangers by analyzing massive volumes of data and finding trends, improving an organization's overall security posture.

Evolving the Role of Human Security Analysts

Human security analysts' skills are enhanced by AI, allowing them to concentrate on more strategic and difficult duties. AI gives analysts more time to focus on threat detection, vulnerability research, and the creation of preventative security policies by automating repetitive and routine tasks like log analysis and data correlation. A more effective and efficient cybersecurity ecosystem is produced by this joint approach between AI and human experts.

Improved Accuracy and Reduction of False Positives

Traditional rule-based systems may miss small indicators of cyber dangers that AI algorithms can spot by analyzing large datasets. The accuracy of threat detection is increased as a result of this capacity to distinguish complicated patterns and abnormalities. AI helps lighten the load on cybersecurity teams by reducing false positives (mistaking benign activity for threats), allowing them to concentrate on real threats and lowering the likelihood that they will miss important signals.

In essence, there are numerous advantages to AI for detecting cyber threats. AI-powered solutions greatly bolster cybersecurity defenses by speeding up threat detection, automating response procedures, enabling proactive security measures, and improving the function of human analysts. Utilizing AI is becoming more and more important in protecting digital assets from ever-changing cyberattacks as the threat landscape changes.

Use cases / Case studies

Case Study 1: City of Las Vegas Digital

Las Vegas Smart City - Using AI to Protect Critical Infrastructure

Issue: In light of growing cyber threats, the City of Las Vegas sought to strengthen the security of its vital infrastructure.

Solution: A cybersecurity platform powered by artificial intelligence (AI) from Darktrace was adopted by the city. The technology makes use of AI algorithms to analyze network data and find anomalies that could be signs of online danger. It keeps an eye on the network infrastructure of the city, including the traffic lights, security cameras, and other crucial services.

Impact: The City of Las Vegas was able to identify and address cyber threats in real-time thanks to the AI system. It discovered irregularities and harmful activity that could have gone unreported otherwise. The city's capacity to defend its vital infrastructure against cyberattacks has greatly increased with the adoption of AI-powered threat detection.

Case Study 2: Narvar Case Study

Protecting ECommerce - Near Real-time Threat Detection and Response

Issue: In order to preserve its customers' data and maintain service, Narvar, a top supplier of customer engagement solutions for e-commerce enterprises, needed to beef up its security procedures.

Solution: Blue Hexagon's threat detection and response system, powered by AI, was adopted by Narvar. Deep learning techniques are used by the system to scan network traffic and identify known and unidentified threats in almost real-time. Malicious activity is automatically stopped, and security analysts are alerted for further investigation.

Impact: Narvar's capacity to quickly identify and respond to cyber threats was improved by the AI-powered system. For Narvar's e-commerce clients, it helped safeguard customer data, avoid service interruptions, and guarantee a safe and seamless experience. The overall security posture of Narvar's network has been greatly enhanced by the adoption of AI-driven threat detection.

Case Study 3: IBM Case Studies - Andritz

Real-Time Protection in Machinery Industry - Real-time Cyber Threat Detection

Issue: To safeguard its essential infrastructure and production processes from online threats, Andritz, a global leader in the machinery sector, needs to strengthen its cybersecurity safeguards.

Solution: IBM's AI-driven cybersecurity solution was put into use by Andritz. The solution makes use of AI algorithms to instantly assess user activity, system records, and network traffic. It recognizes and responds to potential online dangers like malware, intrusion attempts, and insider threats.

Impact: Andritz's machinery sector infrastructure received real-time protection from the AI-powered cybersecurity solution. It safeguarded the integrity and continuity of manufacturing processes by identifying and preventing cyber threats. AI-driven threat detection was implemented, which improved Andritz's capacity to proactively defend its vital systems from cyberattacks.

These case studies show how multiple companies have improved their cybersecurity defenses by implementing AI-powered threat detection technologies. The AI systems increased overall security posture, real-time threat detection, proactive protection, and proactive protection, all of which helped to defend crucial infrastructure, safeguard customer data, and guarantee continuous operations.

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Conclusion

In conclusion, including AI in cybersecurity procedures can significantly improve cybersecurity measures. It facilitates a pro-active security posture, hastens threat detection, increases containment and reaction capability, and develops the role of human security analysts. Organizations can improve their defenses, spot attacks in real-time, and protect sensitive data and essential infrastructure by utilizing AI's capabilities.

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mohan
Written By

A technology veteran, investor and serial entrepreneur, Mohan has developed services for clients including Singapore’s leading advertising companies, fans of Bollywood movies and companies that need mobile apps.

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