Cybersecurity has been a massive challenge with the onset of the COVID19 outbreak. Many organizations have seen an enormous surge in phishing attacks and ransomware attacks, leading to huge losses. According to a report, Personally identifiable information (PII) related data breaches are the most expensive among others.
Most such information is exposed due to social engineering attacks that influence the users to take specific action installing malware or ransomware. These activities can be anything from downloading a file to sending an email. According to another report, about 98% of cyber attacks rely on social engineering making it one of the most significant cybersecurity risks.
However, modern innovations like Artificial Intelligence are helping organizations cope with such cybersecurity challenges. One of the most significant innovations is predictive AI that leverages Machine Learning(ML) algorithms to offer recommendations on vulnerabilities and enable anomaly detection.
Here, we will discuss how predictive AI enables advanced software security through smart predictions powered by ML algorithms. Let’s begin with what intelligent predictions are.
What are Smart Predictions?
Every smart prediction is a recommendation that helps the organization understand the necessary measures that they can take to avoid specific scenarios. These scenarios are called predictive scenarios, which can differ according to the application of the AI model.
For example, if you are using predictive AI for customer support purposes, the scenarios can be related to specific product or service issues. Considering the cybersecurity scenario, you will have to first define specific predictive scenarios.
To define scenarios, you will need data aggregated from the system through analytical tools like SAP analytics. Once you define the predictive scenario, the next step is to build a predictive model for the comparison of different data patterns.
Based on different predictive scenarios, you need to create models that can offer intelligent recommendations. Once you make the AI model, you need to analyze your data from different resources and compare the results for more intelligent predictions. One of the key aspects of using predictive AI models is that you will have to define key performance indicators.
For example, if you are building a predictive model to track the security of your emails. First, you need to define what type of emails can be spammy and what your system can filter out. For this, you will need historical data of email-based cyberattacks.
However, there are services like GSuite where you can have a specific security layer; adding predictive analytics can enhance the entire cybersecurity of your system. With such advanced analytics and more intelligent predictions, you can detect anomalies in your system.
Here, you can define a vulnerability score to create a customized trigger function to activate specific policies.
Vulnerability Risk Score
A vulnerability risk score measures how much of a security issue vulnerability of your system is. Every vulnerability is an aspect of system functioning, architecture, or a resource that can be a target for hackers to misuse. Therefore, one of the crucial programs in cybersecurity is CVE or Critical Vulnerabilities and Exposures.
It is essential to understand that CVE programs need a context-based approach to be successful. Without context, the CVE program will not yield results. Let’s take an example of a QR code generator tool that helps create unique identification for different components of your system. Now here the context is identification; without the need for you to identify a component, the QR code generator will become redundant.
Source:Â Understanding Vulnerability Scoring to Help Measure Risk (tripwire.com)
CVSS or Common Vulnerability Scoring System is essential for your predictive analytics. You can leverage three groups of metrics like Base Metric Group, Temporal Metric Group, and Environmental Metric Group to create CVSS. This modular approach enables you to merge scoring from all three metric groups and generate a vulnerability score that ranges between 0 and 10
So, you need to define a maximum threshold for CVE and determine the AI algorithm’s context. However, the best approach that you can take is a shift from predictive to prognostics.
Shift Towards Prognostics
Let’s first understand the predictive analytics operation. For example, an e-delivery business uses predictive AI analytics for anomaly detection in its supply chain. It enables them to track any anomalies that may disrupt the supply chain and cause downtime. One reason for the popularity of predictive analytics in industries is its ability to leverage time-data series.
However, with the advent of IoT (Internet of Things), sensors have become cheaper, allowing this business to leverage cloud-based technologies. In addition, due to the efficiency of cloud-based services and pay-per-use models, industries can now afford to install such systems for reliable detection of anomalies.
Predictive analytics is an excellent way to enable security policies in your organization. But, what’s more interesting is the prescriptive analytics that follows the predictive model. It offers organizations smart predictions to avoid any security breaches and vulnerability exposures.
Source:Â WhitePaper_AVEVA_AIfromPredictivetoPrescriptiveandBeyond_02-20 (thefoodtech.com)
Prognostics takes this to another level. It fuses predictive AI with prescriptive analytics to automate decisions for your systems. A prognostic is all about answering questions like,
- What if your system is exposed to a ransomware attack at an on-premise data center?
- Can it survive a DDOS or malware attack?
Merging predictive, prescriptive, and prognostics can help you automate software cybersecurity systems. However, you will need some prerequisites,
- A predictive model based on different scenarios predefined
- An extensive set of data from various resources across the system, along with historical information
- Context related to each policy execution for cybersecurity
- Reliable predictive AI solution that can help automates software security
Cybersecurity policies will include different aspects like validation, authentications, and identification of critical security loopholes. Furthermore, with an enhanced predictive AI-based software security solution, you can enable intelligent prognostics for higher uptime and secure user access.
Conclusion
Whether off-the-shelf software or a customized solution, you will need reliable cybersecurity measures to avoid data breaches and cyber-attacks. Predictive AI can help you cope with the changes in software technologies and offer reliable cybersecurity analytics.
Modern hackers leverage deception to attack your systems and deceive conventional cybersecurity measures. An AI-based predictive model can help through prognostics that detect hidden anomalies and eliminate the threat.Â
