With the escalating data breaches, online businesses are now integrating AI into their operations to safeguard the security of their customers. Businesses that incorporate these security models increase their operational gains. AI security models help companies to enhance user experiences.
AI has dynamic capabilities that online businesses can employ to enhance their cybersecurity. This technology enhances integrity, identifies patterns, makes accurate predictions, and processes large and multiple data sets. Reading on, you will discover how online businesses leverage it to stay ahead.
AI-Powered Surveillance Systems
Technology has enhanced how end users interact with different products and services. For instance, many people can enjoy playing live casino games like Lighting Lotto at their favorite casino sites from the comfort of their homes. These sites use AI-based surveillance systems like facial recognition to prevent fraud and safeguard users’ accounts.
The surveillance systems use audio and images from surveillance cameras to recognize different objects like humans. They employ machine learning algorithms to detect threats that may jeopardize the integrity and availability of online businesses.
AI-based surveillance monitors behavior patterns and can identify unusual activities inconsistent with the established trends. E-commerce also uses these technological trends for fraud protection. These technologies monitor transaction data, payment methods, and customer behaviors to identify unusual activities.
Prevention of Zero-day Exploits
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Zero-day exploits might not have immediate fixes as they are primarily associated with unknown security issues and defects in software. Therefore, attackers utilize these exploits to retrieve sensitive information, disrupting the company’s operations before detection. For instance, IBM has witnessed 7,327 vulnerabilities since 1988.
AI combats this problem using deep learning (DL) and natural language processing (NLP) architectures. DL uses historical data to identify these vulnerabilities and hidden patterns, while NLP scans a source code to identify any inconsistencies or malicious segments. If recognized, NLP will flag off the codes for further examination.
These models become more accurate over time as they understand your context and can identify any potential or underlying threat. NLP also helps to identify suspicious websites and emails. It analyses the emails using keywords used by most phishing texts. NLP also significantly enhances the automation of threat detection tasks requiring manual assessment.
Improved Threat Detection and Response Time
Cyber attacks continue to evolve as technology advances. Threats that cannot be identified without AI and require immediate mediation continue to increase. A recent report showed that 43% of attacks targeted small-scale enterprises, but only 14% were prepared to protect themselves.
Most of these attacks come from outsiders than insiders. Speed is crucial in threat detection since it allows security teams to respond to attacks before they can cause significant damage. By implementing AI, these businesses can reduce the time it takes to respond to an attack. Reduced response time saves resources that a company would incur in case of a breach.
Conclusion
Amid rising cyber attacks, cyber analysts are not left without an option. AI has emerged as a safe landing space as it offers dynamic capabilities for online businesses to enhance the security and integrity of their platforms. Enhanced security is a competitive edge businesses use to stay ahead of the competition curve. AI provides a rapid and accurate assessment of potential threats, reducing the time required for manual investigation.
