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Automated Threat Hunting: How AI Can Help Businesses Spot Shady Network Activity  

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The artificial intelligence market is experiencing rapid growth and is projected to increase twentyfold by the end of the decade, reaching a valuation of nearly two trillion U.S. dollars, up from the current valuation of almost 100 billion U.S. dollars. This growth is revolutionizing how businesses approach cybersecurity strategies, enabling them to identify, stop, and combat threats faster than ever. The continuous development of the technological landscape brings with it security concerns and hazards in network activity, a high cost of evolution that businesses must address.

Businesses managing large amounts of data and those with weak cybersecurity profiles are particularly vulnerable to malicious attacks and breaches. However, as the world moves towards AI cybersecurity solutions, negative system participants must keep up with the rapid progress. Businesses that are well-prepared to integrate AI into their operations are less vulnerable to a wide range of cyber threats and attacks. Data breaches, which saw a 1% decrease in number in 2022 compared to 2021, may continue following this downward trend. An IBM report reveals that companies utilizing AI and automation contributed to a decline in worldwide data breach costs of almost $1.8 million.

AI is no longer just a buzzword. Prevention is better than cure, and AI solutions help businesses address cybersecurity challenges by assisting them in identifying network anomalies before they escalate into security breaches. But how is this possible?

woman on two computers; network activity AI solutions

Tackling cloud misconfigurations

Cloud misconfigurations, including hacks, security breaches, insider threats, ransomware, and other entry points into a network, are a significant concern for organizations. AI is essential in addressing these vulnerabilities, as they account for 82% of data breaches and cost businesses an average of $4.45 million per year.

Even giant corporations are susceptible to cloud security breaches, highlighting the importance of proactive data management and security approaches. Facebook, for example, experienced a cloud security breach in 2019 that was only revealed in 2021 when the incident was publicly disclosed. Detected data breaches can take a long time to identify, and victims may not be promptly notified. AI’s continuous learning and recall capabilities can improve the cloud environment by identifying patterns, analyzing data, making corrective suggestions, and acting as a barrier to intrusion.

Machine learning models designed to identify suspicious activity

Machine learning models, such as supervised and unsupervised models, are effective in identifying fraudulent network activity through various algorithms. Supervised models can help identify anomalies in the network through techniques like Random Forest, Logistic Regression, and Decision Tree, while unsupervised models identify trends and patterns in raw datasets and process vast amounts of data.

AI makes use of historical data to understand patterns

AI tools utilize historical data to understand patterns and trends in previous fraudulent transactions, aiding in the prevention of future threats. For example, NVIDIA uses deep learning and pattern recognition to design products, showcasing the potential of AI in identifying patterns across different sectors.

Overall, AI is reshaping the cybersecurity landscape with its anomaly-spotting powers. With the ability to self-train, adjust, and identify risks in real-time, AI-backed tools can reduce exposure to ever-sophisticating cybersecurity threats in network activity.

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