Automated Threat Hunting: How AI Can Help Businesses Spot Shady Network Activity
Automated Threat Hunting: How AI Can Help Businesses Spot Shady Network Activity
The global artificial intelligence market is growing by leaps and bounds. It is expected to increase twentyfold by the end of this decade, valuing it at just under two trillion U.S. dollars, up from its current valuation of almost 100 billion U.S. dollars. It is revolutionizing how businesses approach cybersecurity strategies, empowering 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 pay.
Organizations managing large amounts of data and those lacking a solid cybersecurity profile are most susceptible to malicious attacks and bad actors entering their gates. However, as the world takes steps towards AI cybersecurity solutions that help them manage attacks and threats in network activity, negative system participants must keep up with the rapid progress. The better-prepared businesses are to welcome AI into their everyday operations, the lower their vulnerability to the 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 taking advantage of AI and automation contributed to a decline in worldwide data breach costs of almost $ 1.8 million.
AI is no longer a buzzword or something to wrap your head around. Prevention is better than cure, and AI solutions help businesses address cybersecurity challenges by assisting them in identifying network anomalies before they escalate into full-blown security breaches. But how is this possible?
Tackling cloud misconfigurations
Misconfigurations in the cloud represent anything that counts as a failure, error, gap, or glitch during cloud-product usage. Examples include but are not limited to hacks, security breaches, insider threats, ransomware, and other entry points into a network. This is a sector where AI is massively necessitated because these types of vulnerabilities were found to take a significant chunk out of organizations’ profits, accounting for 82% of data breaches and costing businesses an average of $ 4.45 million yearly.
Cloud security breaches are common even among giant corporations, demonstrating that data management and security must be proactively approached. Facebook, for instance, went through a cloud security breach in 2019 that wasn’t exposed until 2021, when the company made the incident public. The details involved ranged from user names and phone numbers to email addresses, and the platform’s reputation was severely tainted. Detecting a data breach can take a long time, and victims may not be notified right away or even never find out about the incident. In other situations, victims may be made aware their identity was stolen and potentially suffer wide-ranging repercussions. While this is by no means an easy thought to confront, victims can find comfort in claiming compensation from the party that mismanaged their data. More information about how victims can make data breach claims against a company can be learned at www.databreachclaims.org.uk.
Needless to say, AI’s capacity to continuously learn and recall can improve the cloud environment by finding patterns and conducting analysis based on collected data. Another way it can address vulnerabilities is by making corrective suggestions, exposing threats, and acting as a barrier to their intrusion. Dubious activity can be spotted and stopped in its tracks, as you’ll further discover.
Machine learning models designed to identify suspicious activity
Machine learning models are among the most effective solutions for identifying fraud in network activity through various algorithms. There are two approaches involved: the supervised and unsupervised models. The former can help spot anomalies in the network through three techniques: Random Forest, Logistic Regression, and Decision Tree.
The former algorithm from the enumeration improves scalability, robustness, and accuracy in data interpretation. Logistic Regression is another helpful tool. It has predictive capacities and examines the relationship between different variables to assess the parameters of logistic models. The latter is helpful for both regression and classification models. Plus, it is used to make projections depending on how other questions were previously answered.
On the other hand, the unsupervised model refers to trends and patterns in raw datasets. Additionally, it is used when there are vast amounts of data to process. As the name suggests, solution developers are spared from the need to monitor the model because it can function independently and track unidentified data and patterns.
AI makes use of historical data to understand patterns
The capacity of AI tools to grasp context helps pinpoint trends and patterns in previous fraudulent transactions. AI assists administrators in finding solutions by exposing how different malicious activities have emerged and solutions in the past. By assessing recorded historical data, it boosts the prevention process in the future.
Here’s an example of a company that uses AI for its potential to identify patterns. The giant GPU producer NVIDIA utilizes deep learning and pattern recognition to design and create products. These can include robotics and cars with high task efficiency. Deep learning, a subsector of machine learning, is recognized as one of the groundbreaking technological discoveries of the decade. It has at its core artificial neural networks to complete extensive equations. A wide range of sectors uses this machine learning model, from agriculture to healthcare to financial services. Take the former category, for instance. Deep learning monitors satellite images and weather conditions, discovers soil diseases, enhances resource management strategies, and ultimately improves crop quality.
All in all, AI is reshaping the cybersecurity landscape with its anomaly-spotting powers.
As the technological landscape expands, more and more AI solutions are expected to emerge. We are, for instance, already living in a cloudy world driven by cloud computing which facilitates business data storage and access. This accessibility expedites businesses’ switch to automation. And it opens the door to more malicious actors hunting companies’ and people’s data to compromise it. Therefore, this area is anticipated to be improved by AI capabilities.
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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