10 Reasons to Use AI in Your Cybersecurity Practices | Entrepreneur

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Artificial intelligence (AI) is omnipresent in the digital landscape, pervading numerous industries to improve efficiency, enhance data security, elevate user experiences and boost profits.

In addition to improving business operations, AI can help businesses improve their cybersecurity and offer effective threat detection and response. AI’s ability to process vast and intricate datasets, uncover hidden patterns and deliver precise predictions makes it an invaluable tool for defending against cyber threats.

According to an IBM study, in 2022, organizations were 13% more likely to have adopted AI than in 2021. It indicates an expanding interest in embracing AI and automation as businesses endeavor to strengthen their security posture and stay ahead of potential threats.

Here, I discuss the top 10 advantages of integrating AI with cybersecurity practices.

1. Enormous data handling capability

Given the massive data streams between users and businesses, cybersecurity experts have traditionally relied on filters and rule-based systems to detect anomalies and analyze potential threats in real-time. However, with the ever-increasing scale and intricacy of modern data, these traditional methods are no longer sufficient.

In this respect, AI-powered systems, leveraging machine learning algorithms, offer highly efficient and accurate analysis of enormous data sets generated by diverse business activities. Furthermore, AI continuously learns from data patterns and adapts detection techniques, helping to stay ahead of evolving threats and emerging attack vectors and, in turn, making them invaluable in safeguarding business networks and sensitive information.

2. AI models improve with more real-world data

Through iterative training and exposure to new data, AI models can enhance their performance over time to tackle more challenges, including threat detection. For instance, an AI model trained for anomaly detection using historical data can analyze and understand new patterns in network traffic, enabling it to better identify and respond to emerging threats.

Furthermore, AI algorithms can also leverage human feedback to enhance their performance. By incorporating insights provided by experts or end-users, AI systems can learn from these inputs and refine their decision-making processes.

While AI algorithms can adapt to new threats, their adaptability often requires ongoing research, monitoring and manual intervention. Regular updates and retraining may be necessary to ensure their effectiveness in dynamic and ever-changing security environments.

3. Enhanced endpoint security

Endpoint security is crucial in protecting devices such as computers, mobile phones and IoT devices. However, with the increasing number of endpoints, traditional security measures may not be sufficient as relying on static rules and signatures to detect and prevent threats may struggle to adapt to the rapidly evolving tactics and techniques employed by cyber criminals.

Additionally, the diverse range of devices and operating systems present different security challenges, as each may have unique vulnerabilities that can be targeted.

To effectively protect against this expanding attack surface, more advanced AI-based security solutions, such as behavior-based detection and real-time threat intelligence, can be implemented. These approaches can provide proactive and adaptive protection to safeguard endpoints from a wide range of threats.

4. Boosted threat detection and response speed

The benefits of using AI in cybersecurity go beyond just improving accuracy; AI can significantly improve time efficiency as well. For instance, research reports have shown that AI can reduce the time it takes to identify security threats and breaches by up to 12%, allowing security teams to react quickly and minimize risks.

AI also helps reduce the time taken to remediate a breach or implement patches in response to an attack by up to 12%, saving time and resources and minimizing potential damages.

5. User and entity behavior analytics

User and entity behavior analytics (UEBA) utilizes machine learning to detect anomalous user behavior. It records and stores data points like usernames, activity logs, computers accessed and IP addresses and then uses this information to create a baseline of user behavior.

This baseline acts as a point of comparison for the AI to identify patterns and irregularities in user behavior. For instance, it is more likely to be a malicious action if a user suddenly accesses a lot of files or logs in from an uncommon IP address.

The UEBA system will send alerts if any oddities are found, allowing more investigation into the issue. In addition, UEBA systems can monitor for insider threats, as they can detect when an internal user’s behavior deviates from the norm. This aids businesses in identifying and managing possible security concerns before they become a serious problem.

6. AI-driven personalization and security

Businesses, driven by the need to navigate the constantly evolving cybersecurity landscape, are not only focusing on security but also on user engagement. This has led to a revolutionary shift in their strategies with the integration of AI-powered Identity and Access Management (IAM) solutions.

With AI, businesses can optimize their approach to ensure a seamless and customized user experience, all while maintaining a robust security posture. One prime example of this is adaptive authentication. By harnessing machine learning algorithms, adaptive authentication continuously analyzes and evaluates user behavior and context, effectively assessing the risk associated with specific activities.

Based on this risk assessment, the system dynamically adjusts authentication requirements and prompts for additional verification factors as necessary. This personalized approach minimizes unnecessary authentication steps for low-risk activities, providing a smoother and more streamlined experience. Meanwhile, for high-risk actions, additional layers of verification can be seamlessly introduced, reinforcing security without affecting user experience.

7. Effective detection of false positives and false negatives

AI systems can effectively minimize false positives and false negatives, typically produced by security systems usually built under strict regulations.

False positives squander time and resources by reporting normal activity, which might create alert overload and fatigue. False negatives, on the other hand, may lead to malicious operations going undetected and causing damage.

AI systems trained on vast, evenly distributed and unskewed datasets can better distinguish risky and acceptable activity and respond to new and emerging risks.

8. Prevent zero-day exploits

Zero-day exploits are unknown vulnerabilities — therefore, they do not have any immediate patches or fixes. Cyber criminals target these vulnerabilities with malware to steal sensitive information or disrupt business operations.

However, by utilizing deep learning architectures and natural language processing techniques, AI systems can play a significant role in detecting zero-day exploits. Deep learning models trained on historical data learn the characteristics of such exploits and then apply that knowledge to find subtle or hidden patterns that might point to zero-day exploits or activities.

On the other hand, natural language processing assists in scanning source code for potentially vulnerable or malicious code segments and flagging them for additional examination. As these models become more context-aware over time, they can recognize patterns that indicate an attack is imminent or underway.

9. Threat intelligence

Threat intelligence is the process of collecting, analyzing and scrutinizing data about both existing and possible threats. In other words, this process helps in getting a comprehensive understanding of cyber criminals, their tools, motives and TTPs (tactics, techniques and procedures).

Since this process is extremely resource-oriented and time-consuming, incorporating AI-backed techniques can make it simple and straightforward. It involves scraping colossal amounts of data from various sources like traffic logs, social media, cyber forums, dark web forums and much more.

The data collected through these sources is then processed and analyzed using ML algorithms, extracting valuable insights and identifying patterns or anomalies that indicate potential threats. This enables businesses to make data-driven decisions and proactively mitigate risks early on.

10. Cost savings

Businesses at the forefront of adopting AI-powered security technologies can achieve significant improvements, not just boosting security but also resulting in considerable cost reductions.

According to an IBM study, businesses have increased their Return on Security Investment (ROSI) by over 40% while also reducing data breach-related financial losses by at least 18%. By doing so, they are freeing up resources to reinvest in other cybersecurity activities, enabling them to further enhance their security posture.

Given the increasing prevalence of cyber attacks, incorporating AI in cybersecurity practices has become a necessity. With cybersecurity research predicting a whopping annual cost of $10.5 trillion due to cybercrime by 2025, it is imperative for businesses to expeditiously implement AI in their cybersecurity practices.

However, AI alone cannot completely safeguard against cyber attacks. It must be combined with human expertise and vigilance. With this hybrid AI and human approach, businesses can proactively safeguard against cyber attacks and reduce the likelihood of devastating losses as a result.

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