By 2026, the AI-based cybersecurity market is expected to reach $38 billion with a CAGR of 23.3%.
As data gets progressively transferred to digital platforms for storage purposes, the need for securing these databases becomes increasingly crucial. Cybersecurity, the practice for protecting systems from digital attacks, shook hands with artificial intelligence (AI) to create more robust walls against such risks. Due to lack of cybersecurity experts and the increasing number of cyber attacks, the implementation of an AI-driven cybersecurity process is vital.
How AI is Different from Traditional Methods of Cybersecurity
There are several ways AI can modify cybersecurity, which makes it better than the traditional methods. According to recent studies, 75% of enterprises have taken the aid of AI and machine learning (ML) to fight against cyber criminals, amongst other reasons.
- Due to its reliance on signature-based detection systems, traditional methods could only detect known cyber risks, making it blind towards unknown suspicions. An AI-based method can solve this issue by pinpointing the undetermined ones too, credit to ML-based algorithms. These algorithms can analyze historical data and network data to identify potential threats in real-time, which is difficult for humans to see.
- Another important reason for AI methods to win over traditional methods is the former’s ability to constantly adapt and learn. In the face of new threats, ML algorithms can help identify and control them faster and more seamlessly than traditional processes. This guarantees the safeguarding of sensitive information.
Advantages of AI-based Cybersecurity
Malware and Ransom Attack Detection
Malware (also known as malicious software) is a fraudulent code that is injected into a user’s computer to steal and corrupt sensitive information. This can lead to a ransom attack where the perpetrator agrees to give back the information in exchange for a demanding ransom. According to studies, it is believed that AI-based tools have the potential to prevent such attacks by 90%.
Phishing Prevention
Another attacking loophole related to malware is phishing. With more cyber attacks, phishing has become a serious threat to privacy. Phishing is a method by which attackers send dangerous links to users (maybe via email), which when opened gives attackers access to misuse private information. Credit to AI and ML, such emerging technologies can detect hazardous links and prevent the incidence of cyber-attacks.
Threat Detection
AI is known to evolve and adapt. Laced with advanced algorithms, AI can help with pattern recognition and detect the most microscopic malware and ransomware attacks before they can risk the system. An amalgamation of AI and ML algorithms can help differentiate good bots from malicious bots. AI-based cybersecurity systems can also identify newest threats and curate modern preventive measures. It can offer information regarding industry-specific and global dangers to build prioritization decisions.
Risk Prediction
Prescribed analytics from AI-based analysis can help in boosting resilience against cyber crime. By studying the IT asset inventory, it gets easier to forecast the allocation of resources to affected areas. For instance, large companies have quite an extensive and complex IT asset inventory. Analyzing every content manually or using the help of traditional methods can fail to detect risks and solve it effectively. Thus, cognitive learning-based models pose to be efficient in fraud detection and early resolution of remote hacks, notify users, and build robust security levels.
Boosted Efficiency
AI-powered automation helps analyze large amounts of security data effortlessly while also detecting malicious activity. AI tools can also look for vulnerable spots that attackers can take advantage of. It boosts patch management and streamlines incident response processes. Credit to AI algorithms, the severity and influence of security breaches can be easily detected by studying relevant data. Deep learning algorithms also help in identifying sensitive data and ensure the prevention of its leakage.
Network Traffic Analysis
Deep learning algorithms are effective in handling Distributed Denial of Service (DDoS) attacks. DDoS is a type of cyber attack where the perpetrator uses an array of systems and overwhelms specific networks with traffic, making them unreachable to users. Deep learning tools can thus analyze the legitimacy of network traffic, and take proper measures to curb them. Its ability to control network flow and traffic patterns helps with seamless flow.
Cost Reduction
With increased accuracy in detecting threats through AI automation, companies can save on unnecessary costs and time. Routine tasks that were repeatedly performed by humans can now be simplified by AI tools, leading to faster and error-free execution of tasks.
Importance of Data Science Services in Cybersecurity
Before the introduction of data science services, cybersecurity solutions were majorly based on human assumptions. This led to a fear of whether these assumptions would be fruitful or not. Since leaving sensitive data in the hands of assumption is not the ideal scenario, data science made cyber security better by making decisions based on facts and data. With its accurate predictions, data science services made better decisions based on hard facts rather than what-ifs. Thanks to these data-based tools, cybersecurity professionals can have a much easier time blocking such risks while making informed decisions. Although data science cannot reduce the instances of cyber attacks, it can definitely make tackling them seamless and manageable.
Business intelligence Laced with Cybersecurity
Business intelligence (BI) has proved to be quite useful, credit to its ability to analyze and cleanse data. This helps in understanding approaching trends and can be used for predicting risks and loopholes. It is used in fields like risk management, logistics, and marketing. As cyber security analysts work to find loopholes for cyber attacks, BI can help harden these vulnerable spots with available bias-free data. BI can filter out the unnecessary details while keeping the vital ones when creating Big Data. Visuals like graphs can then be pulled based on the information present in the database, which can furthermore help with anomalies.
Challenges Faced by AI
Any technological invention can be a blessing and a curse. Along with the many life-saving offerings, AI-based cybersecurity can have a few cons, which can be prevented if used appropriately.
Let’s look at some of the challenges that one can face with AI-based cybersecurity –
Manipulation of Data
Since AI works with historical data, hackers can gain access and manipulate training data to their own advantage and adversely affect its efficiency.
Affected Privacy
As AI solutions enhance, AI-based cyber attacks also rise in number. Hackers can develop malware that can avoid detection and create havoc. Without proper encryption, sensitive data can fall prey to such attacks.
Unavailability of Data
AI models operate with high-quality data. Hence, it is crucial to have bias-free, sufficient data for the AI system to function adequately. If failed to generate such data, it would lead to an ineffective model leading to a lack of security and the rise of undetected threats.
Although the challenges are grim, with the right robust infrastructure, countering such risks and disadvantages can be less daunting.
Conclusion
As AI and ML-driven solutions started yielding better results, more and more companies took aid of such technologies to fight against malware and cyber threats. At Josh, you can rest assured that our experts will curate the perfect AI-based cybersecurity plan for you. With our data analytics services, we can help with accurate forecasts of potential cyber risks, and produce reliable, assumption-free data that help in making better decisions.
