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AI-Driven - Predictive Analysis

Recent research shows that the average cost of a cyber breach in 2022 was $4.35M. It is predicted that cybercrime is expected to rise to $10.5 trillion by 2025. [Forbes]


This prediction is another place where innovative AI and machine learning products can become handy. In the short time that AI and Machine-Learning products penetrated our daily lives, they already managed to change how we work dramatically, for good and for bad.

Similar to AI and ML products' ability to predict what we want to write, paint, design, or develop, it can provide predictive analysis and find anomalies in places the human eye might miss.


AI-driven predictive analysis in cybersecurity can leverage its advanced technology to analyze vast amounts of historical data, identifying patterns and anomalies. This proactive approach allows organizations to anticipate potential threats, vulnerabilities, or breaches, ensuring a more robust defense mechanism. As cyber threats become more sophisticated and evolve rapidly, AI-driven predictive analysis offers a dynamic tool to stay ahead of potential security challenges, ensuring timely interventions and strategic defense planning.

Let's drill down into three different examples of threats that AI tools can help resolve.



1. Use Case: Application Vulnerability Management

Application Vulnerability Management involves identifying, classifying, and addressing application weaknesses. Malicious actors can exploit these vulnerabilities to manipulate application behavior, leading to potential data breaches or unauthorized actions. The process of Vulnerability Management is an endless game.


While applications evolve and become more complex, they can introduce new vulnerabilities. If these vulnerabilities are not addressed promptly, cybercriminals can exploit them, leading to potential data breaches, unauthorized transactions, or application downtime.


AI-driven predictive analysis tools like Darktrace, Vectra, checkmarx, or balbix can analyze past application vulnerabilities, patching patterns, and threat landscapes. By doing so, it forecasts potential future vulnerabilities within applications. This proactive approach lets developers and security teams prioritize patching or updates, ensuring more robust application security. Tracking these vulnerabilities manually and analyzing them across various applications is time-consuming and prone to human errors. It's challenging for individuals to keep up with the constant influx of new vulnerabilities and to predict potential future threats within applications.


AI can "simply" process vast amounts of application data at high speeds, identifying patterns and trends that the human eye might miss. By analyzing historical data on application vulnerabilities and patching patterns, AI can provide predictive insights into potential future vulnerabilities, allowing organizations to take proactive measures.




2. Use Case: Advanced Persistent Threat (APT) Detection

Advanced Persistent Threats are prolonged and targeted cyberattacks where attackers exploit an application vulnerability and remain undetected for an extended time. These sophisticated attacks can lead to significant application breaches or unauthorized actions. Attackers exploiting application vulnerabilities can remain hidden, manipulating application behavior, stealing sensitive data, or causing other damage.


Detecting APTs within applications requires 24/7 monitoring and analyzing application data and interactions. Given the stealthy nature of APTs, it's easier for individuals to identify these threats with advanced tools. Some monitoring tools are based on rules configuration that aims to find anomalies in the application, unauthorized logins, irregular usability in suspicious working hours, etc. As applications continue to develop and require new features, there is an ongoing need for more advanced monitoring capabilities.


Advanced AI monitoring products can continuously monitor and analyze application data, identifying patterns or anomalies that might indicate an APT. By providing real-time insights and predictive analysis, AI helps organizations detect and respond to APTs within applications more effectively.


AI algorithms can analyze vast amounts of application data and interactions, identifying subtle patterns or anomalies consistent with APT behaviors within applications. By predicting these threats, developers and security teams can take early action, reducing the potential damage of an APT.


3. Use Case: User Behavior Analytics for Application Access Control

User Behavior Analytics for Application Access Control involves monitoring and analyzing user behaviors within applications to ensure they only perform actions relevant to their roles, maintaining application integrity and security.


Unauthorized actions within applications can lead to data breaches, unauthorized transactions, and other security incidents.


Manually monitoring and analyzing user behaviors within applications can be tedious and may only capture some unauthorized actions. Predicting potential security breaches based solely on past behaviors within applications is challenging.


AI-driven predictive analysis can forecast potential unauthorized actions by analyzing historical user behavior patterns within applications. This allows for dynamic application controls, where user permissions within applications can be adjusted in real time based on predictive insights, ensuring tighter application security controls.

AI can analyze applications' vast amounts of user behavior data, identifying patterns that might indicate potential unauthorized actions. By providing predictive insights, AI allows organizations to adjust user permissions within applications in real time, enhancing security measures and preventing potential breaches.



In conclusion.

AI-driven predictive analysis is poised to play a pivotal role in enhancing cybersecurity measures and defending against the escalating threat landscape. The astronomical cost of cyber breaches and the projected exponential increase in cybercrime underline the urgent need for advanced tools and technologies to protect organizations and individuals.


AI and machine learning products have already demonstrated their transformative potential in various aspects of our lives, and their application in cybersecurity is no exception. By leveraging AI's ability to analyze vast amounts of historical and newly generated data, identify patterns, and detect anomalies that may evade human observation, organizations can proactively anticipate potential threats and vulnerabilities.


As cybersecurity landscape rapidly evolve and threats are becoming more sophisticated, AI-driven predictive analysis offers a crucial advantage. Its ability to process vast amounts of data, detect emerging patterns, and predict potential threats positions it as a dynamic and indispensable tool for safeguarding digital assets. As organizations face the daunting challenge of defending against an ever-expanding array of cyber threats, embracing AI-driven predictive analysis is a proactive and strategic move toward bolstering security measures and staying ahead of evolving risks.



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