In the current times, cyberattacks have become more sophisticated and advanced. Traditional methods of cybersecurity are not enough to tackle this advancement in cybercrime.
AI technology can be used as a proficient tool to prevent and avoid online fraud and scams. Today’s AI systems are trained to analyze large amounts of data, identify anomalies and vulnerabilities, and automate repetitive processes. All these AI features are significantly helpful in preventing cybercrime.
It has become a common occurrehttps://mediapract.com/story/articles/best-mobile-games-available-on-app-store/nce to hear about cyber threats and ransomware, which is a concerning matter and a wake-up call. One such incident happened in AIIMS New Delhi in November 2022.
AIIMS Ransomware Attack
All India Institute of Medical Sciences, Delhi was hit by a ransomware attack in November 2022, the attackers targeted the organization’s sensitive information including patient reports, research data, and administrative information.
It severely disrupted the environment of the hospital, leading to disturbances in the delivery of medical services. This incident raised alarm bells around global cybersecurity practices and highlighted the need for improvement in cybersecurity.
Cybercrime affects millions of people every year. The following graph illustrates the reported losses due to cyber crimes in the past decade.
We can use AI in the following seven ways to strengthen the cybersecurity systems
AI can be a vital tool to improve cybersecurity. It can be used in the following seven ways to enhance security online –
1. Real-Time Threat Detection – Threat detection is a vital component of cybersecurity. Threat detection methods rely on analyzing previous malware attacks and potential dangers that might compromise the network.
AI excels at analyzing data and records from network traffic, system logs, and user behavior. AI-driven threat detection systems can rapidly and easily analyze vast amounts of data. Due to the automation of analysis through AI, security analysts get more time to focus on more complex investigations.
AI identifies patterns from past attacks that help the system predict potential future attacks. These warnings and predictions enable the security team to take better preventive measures. This proactive method of detection significantly helps the overall security posture.
2. Advanced Malware Analysis – The traditional methods of malware analysis such as heuristic analysis, signature-based detection, sandboxing, and the use of black-and-white lists have had significant impacts on the detection of malware in the past years. But they have several limitations too.
Traditional methods are of little use in front of increasingly sophisticated attacks. AI assists the system in analyzing huge amounts of data to make predictions and smart decisions.
By sorting and analyzing different forms of data, machine learning models become capable of distinguishing benign software from potential malicious ones.
Some deep learning models can also learn a malware’s hierarchical representation of malware samples. This enables the system to identify complex patterns and correlations that can help in detecting malware.
3. Enhanced Phishing Detection – Artificial intelligence and machine learning programs look for anomalies and suspicious activities throughout the potential gateways of phishing.
Cyberattackers commonly use messages and emails to trap a user or get through the security, AI algorithms can examine and analyze complete emails, warning the users or blocking the email account if anything suspicious is detected.
AI constantly learns from previous activities, users, and administrators’ feedback, which helps it become more accurate in the detection of such traps.
4. Automated Incident Response – Incident response refers to addressing any incident that disrupts and might harm normal activities, services, or performance.
AI can identify and detect any out-of-the-place activity more efficiently and send warnings and notifications to the user before it becomes a full-blown attack and harms the network.
Modern automated incident response systems analyze incident data, which helps provide a better understanding of incidents that will help the system to trace and learn patterns and take rapid action if any activity of that sort is repeated.
5. Continuous Network Monitoring – The use of AI in network monitoring eliminates fog associated with system trafficking, which helps in root cause analysis.
AI tools monitor and analyze network traffic patterns and help significantly in detecting anomalies, predicting a problem, and preparing suitable action for it. It also reduces the false positive alerts that can overwhelm analysts.
6. Intelligent User Authentication – Intelligent user authentication ensures that the sensitive information or personal details of an individual are safe and secured.
The usage of facial recognition, thumbprints, and other biometric identification is common these days. Biometric authentication uses deep learning techniques to confirm one’s identity and decide whether to give access to the information or not.
There are two types of biometrics in authentication: biometric and behavioral biometric authentication.
Biometric authentication includes fingerprint, face, and iris recognition while behavioral biometric authentication includes behavioral patterns such as way of moving the mouse, clicking, typing, etc. AI assists in both of these methods of authentication making the process faster, efficient, and more reliable.
7. Predictive Analytics for Threat Prevention – AI algorithms use machine learning models to analyze and learn from past data. These models are trained on historical data which helps to identify and pick patterns and relationships that help in making predictions for future outcomes.
There are three core components of AI predictive analysis-Data, algorithms, and predictions.
Data is the foundation of the whole process. It should be relevant, comprehensive, and accurate
Algorithms are like complex mathematical models that learn from the data and act as the brain of predictive AI
Predictions are the result of data analysis, they can be of many types, from forecasting future trends or identifying potential anomalies. They help you make a smart and informed decision.
DO YOU KNOW? Human error is the primary cause of cybersecurity breaches, accounting for 68% of all data breaches.
With the advancement of technology, the intensity of cybercrime is also increasing, This calls for stronger preventive methods and precautions. Traditional security methods are no match in front of today’s cyberattacks.
It has become essential to include AI techniques in security programs to ensure the safety of data. AI has the capability and power to address the issues that traditional methods often miss.
Today, AI and cybersecurity are more connected than ever, and it is safe to say that in the coming years, the use of AI in cybersecurity will only expand. Also, learn about EMF Hat Blocks Radiation by reading this article.
