Top Reasons to Use Predictive AI for Enhanced Cybersecurity in 2021
The top reasons to use predictive AI for cybersecurity enhancements include:
- Improved detection accuracy.
- Sensitive risk intelligence.
- Automated decision-making.
The recent pandemic has amplified the need for advanced cybersecurity measures. There have been several instances of cybercriminals taking advantage of the situation by posing as World Health Organization’s officials.
Top investigative agencies in the United States like the FBI have reported an increase of 300% in cyberattacks since the COVID-19 outbreak. Most of these attackers use deception, which is why predictive artificial intelligence (AI) becomes essential for cybersecurity.
A predictive AI model collects data, analyzes and offers recommendations that can prevent various cyber attacks. Many organizations reconsider using Artificial Intelligence due to the high initial cost and need for infrastructure. However, according to an IBM report, businesses lost $3.86 million in 2020, with a total of more than 200 days spent on finding the actual breach. In 2021, data breach costs rose from $3.86 million to $4.24 million, the highest average total cost in the 17-year history of that report.
Such massive losses have helped organizations realize the need for reliable AI development to enhance cybersecurity. Here, we will discuss how predictive AI can enable cybersecurity measures through intelligent analytics and threat detection. But, first, let’s understand predictive AI.
Predictive Artificial Intelligence
Predictive AI, also known as the third wave AI was first coined by DARPA or Defense Advanced Research Projects Agency. It was developed as an intelligent tool that can help organizations cope with cyber threats before they occur. Third-Wave AI is used in Security Operation Centers (SOC) and works in real-time; it helps defend against data breaches, malware, and ransomware attack.
There are three types of AI algorithms that you can leverage for cybersecurity purposes:
Supervised models need human supervision for the training algorithms through analysis of data patterns. When used for cybersecurity, this results in recommendations to keep your network and data safe.
The approach almost resembles how you teach a child. For example, you show different images with symbols and what each of them stands for. Then, when you ask complicated questions related to any random symbol from the same database, they can detect the relative information.
Unsupervised algorithms do not need any human intervention for training and can provide predictive suggestions for preventing cyberattacks. It is a self-learning approach where the algorithms train and find data patterns that are not easy for humans to search.
For example, a generative model is an unsupervised learning method, where algorithms imitate training data generation. You can leverage a generative model to replicate data regarding earlier cyberattacks and prevent further threats through unsupervised training.
Reinforcement models are different from supervised and unsupervised algorithms. Here, you don’t have to provide examples to the algorithm for training. Instead, you provide a guide or method to optimize performance in specific scenarios. You can train the algorithms for infinite possibilities without the need for human interventions.
An AI development based on a predictive algorithm can help build intelligent cybersecurity measures that detect anomalies and prevent intrusions.
The conventional layered approach of cybersecurity is not enough to cope with modern threats like deceptive attacks and ransomware. Moreover, these approaches cannot detect internal threats of a system which are not easy to find. However, with predictive algorithms and advanced analytics, you can improve detection accuracy.
For example, one of the most significant cybersecurity challenges is to detect anomalies. Using the unsupervised AI model, a radio-frequency-based anomaly detection system can help you see signal-to-noise ratio (SNR) which are usually found in Industrial settings where SCADA systems and Programmable logic controllers are in use. It detects the cybersecurity anomaly through Convolutional Neural Network (CNN), which offers raw spectral data on Long Short Term Memory (LSTM) networks and is usually associated with Deep Learning.
The unsupervised algorithm analyzes raw data and characterizes the prediction error as Gaussian distribution. So, you get a curve representing the abnormal levels of network traffic indicating an anomaly.
Apart from cyber threats and anomaly detections, AI-based algorithms can also help you with predictive risk intelligence.
Predictive Risk Intelligence
Integration of predictive analytics and risk intelligence can help you reduce cyberattacks. Predictive risk intelligence offers several benefits like risk minimization, decision-making for specific policies, and automation of processes.
Decision-making for Risk Management
A predictive algorithm can analyze large volumes of contextual data and crucial touchpoints for making rational choices. Predictive risk intelligence can offer insights into cybersecurity that enable executives to make strategic and financial decisions.
You can leverage AI development based on predictive algorithms, which offer risk intelligence on integration at specific touchpoints. For example, if you need to integrate several third-party services, you need to have security policies for data access to prevent breaches. Here, you can leverage predictive risk intelligence to create reliable security policies.
Risk Sensing Process
The risk sensing process is about detecting cyber threats that humans and rule-based algorithms do not easily detect. A rule-based algorithm works on a specific set of instructions, and that is why it is restrictive in detecting new anomalies.
This process allows organizations to find new categories of anomalies, risk assessment, and future risk predictions. For example, your organization can integrate customer relationship management (CRM) into different social media platforms through APIs. With a risk sensing process through predictive AI, you can detect any issues with such integrations.
Automations in Threat Monitoring
Threat monitoring deals with analyzing several structured and unstructured data, which is a labor-intensive and error-prone activity. However, with AI development powered by predictive algorithms at its core, you can automate threat monitoring activities.
Threat monitoring efficiency can also enable credit risk management and model risk management for your organization. Thus, it reduces the number of financial risks and process-based losses due to cyberattacks.
Predictive AI can take your cybersecurity capabilities to the next level. However, there are specific requirements that you need to consider before implementing and investing in AI development for your organization. The best practice is to follow a phased approach that allows you to integrate predictive AI at different levels of the organization. You will have to:
Redefine accountability in the organizational structure.
Define the control framework.
Start small and scale faster.
Automate monitoring and detection.
The idea behind a layered security defense is that an attacker has more protection mechanisms to bypass, faced with such obstacles they will give up.
Also, if the AI control malfunctions, fails, or has been misconfigured, then a layered defense would offer additional protection and provide your security teams with a comprehensive defense against security threats.
A phased approach with predictive AI algorithms will help enhance security, detect future risks, and avoid financial losses.