Are you ready for a cyber attack?

Cybersecurity risks are becoming more complicated as the digital landscape changes. Protecting data and systems from sophisticated phishing techniques and advanced persistent threats (APTs) is becoming an increasingly difficult task. AI has become an essential ally in the fight against cyber threats in this high-stakes environment. AI is changing the way businesses approach security by improving their capacity to recognize, evaluate, and react to cybersecurity alarms. Even though artificial intelligence (AI) has many benefits, it’s important to be aware of any potential risks and usage restrictions.

The Increasing Complexity of Cybersecurity Threats

The complexity and prevalence of cybersecurity threats are increasing, as hackers use cutting-edge methods to get beyond established defenses. These dangers are so sophisticated and numerous that manual monitoring and reaction are insufficient. Because of this, businesses are depending more and more on AI to assist in handling and automating how they prosecute security alerts.

Role of AI in Enhancing Cybersecurity Efforts

AI is essential to modern cybersecurity because it can be used to automate threat detection and analysis. With its help, businesses can analyze enormous volumes of data in real time and spot trends and abnormalities that can point to a security breach. Security teams can concentrate on the most important problems by using AI-driven solutions that prioritize alarms according to their severity.

Leveraging AI for Cybersecurity Alerts

AI Tools and Platforms for Threat Detection

A number of AI-powered and AI-enhanced platforms have become indispensable resources for cybersecurity experts. For example, CrowdStrike leverages AI to evaluate billions of events daily, detecting and removing risks instantly. Another well-known tool, Darktrace, uses AI to track network traffic and identify possible risks based on patterns in behavior.

How AI Analyzes and Prioritizes Alerts

AI systems are particularly good at filtering through massive datasets for possible dangers. They can rank warnings according to risk, including the chance of a breach and its possible consequences, after analyzing incoming data against known threat signatures and behaviors. AI contributes to the reduction of noise caused by false positives freeing up security staff to focus on actual threats.

Examples of AI-Driven Threat Detection in Action

AI-driven threat detection has shown to be quite successful in real-world applications. AI systems, for instance, are able to identify minute variations in network data that could indicate the beginning of an attack. In one instance, AI discovered a string of minor incursions that, when combined, showed a deliberate attempt to compromise a network. These discrete incidents might have gone undiscovered in the absence of AI, resulting in a successful breach.

Benefits of Using AI in Cybersecurity

Faster Detection and Response Times

The speed at which AI can identify and react to attacks is one of its most important advantages in cybersecurity. AI technologies enable businesses to respond quickly to any breaches by processing and analyzing data at a rate significantly faster than that of human beings. AI doesn’t need breaks or rest, meaning it can continue monitoring and responding to threats even while threat analysts sleep, ensuring that potential breaches are addressed immediately.

Improved Accuracy and Reduction in False Positives

AI’s ability to absorb new information, adapt, and learn from historical data significantly enhances threat detection accuracy. This precision reduces the frequency of false positives, which can otherwise drain resources and cause alert fatigue among security staff. By minimizing unnecessary alerts, AI allows security teams to focus their efforts on addressing real threats. When a security team is so consumed by following up on alerts that they cannot focus on recovery actions and process improvement, their effectiveness is limited and they can easily miss a critical alert.

Pitfalls of Over-Reliance on AI

In a previous blog posting, I discussed over-reliance on AI, namely how it may provide a false sense of security and frequently misses the importance of human interaction in decision-making processes. Another potential pitfall can be the lack of availability of an AI system that an organization has become entirely dependent on for its processes. If an AI system responsible for processing all critical cyber alerts goes down, then no alerts can be processed while the system is down. This means it’s important to have some kind of backup manual process in place so that cybersecurity alerts are not missed during an AI malfunction. 

Risk of Missing Novel or Sophisticated Attacks

While AI is great at detecting known dangers, it can struggle with unique or extremely sophisticated attacks that do not follow recognized patterns. These risks may fall through the cracks if AI systems depend too heavily on predetermined algorithms and signatures. To mitigate this risk, there needs to be regular review of alerts and data that were ignored by AI to ensure that the ML is properly tuned. 

Dependence on AI Algorithms and Potential for Errors

AI systems are only as good as the algorithms and data they are built upon. If these algorithms are defective or the data is out of date, the AI may generate inaccurate or incomplete findings, potentially leading to missed dangers or false alarms. An example of this could be an alert that was classified as low so that no follow-on actions were taken, allowing a ransomware attack to spread across the organization which could have been halted via host isolation.  

Overlooking the Importance of Human Expertise and Intervention

Despite its strengths, artificial intelligence cannot replace human competence in cybersecurity. Human analysts provide context, intuition, and experience, which AI cannot mimic. Over-reliance on AI may result in a false sense of security and even trigger actions based on false positives that could have a detrimental effect on the production environment. An example of this would be a fully automated AI-powered EDR that both removes critical database files and isolates an infected host from the internet when a critical threat is detected. If this were a false positive, it could result in a system outage for an entire company and ultimately in financial loss. It’s important to consider the scope of control a fully AI-automated system can exert over production data without human intervention in the mix and introduce a human layer into the process to prevent accidental outages. 

Best Practices

Combining AI Tools with Human Analysis and Expertise

Some of the most effective cybersecurity techniques combine AI and human skills. AI can perform the hard lifting of data analysis, but human analysts are required to analyze the findings and make informed judgments. Once a human analyst has confirmed an alert is a true positive, their findings can be used to continue training the AI model thereby improving the detection capability in the future.

Regularly Updating and Fine-Tuning AI Systems

To remain effective, AI systems must be continually updated and fine-tuned. This involves providing them with new data, updating algorithms, and ensuring they are up to date on the newest threat intelligence. Cybersecurity threats are continually evolving, so we cannot expect an AI model that was trained a year ago to still be effective at detecting threats that were not even discovered at that time.

Training Staff to Understand and Interpret AI-Generated Alerts

AI-generated alerts are only beneficial if they are understood and implemented effectively. Organizations should spend time training their employees in not only the interpretation of AI alerts but also in the potential items that AI can miss so that the employees know when and how to manually validate the AI decision-making process.

Conclusion

AI has transformed the way businesses tackle cybersecurity, allowing for faster detection, increased accuracy, and fewer false positives. However, it is critical to acknowledge the limitations and risks that come with over-reliance on AI. Organizations may fully leverage AI as part of a complete cybersecurity strategy by integrating AI tools with human expertise and reviewing AI systems on a regular basis to ensure they are using the most updated models.

What do you think about using AI to augment the Cybersecurity of your organization?

References

Skwarczek, B. (2023). Using AI In Cybersecurity: Exploring The Advantages And Risks. In Forbes.com. Forbes. Retrieved August 15, 2024, from https://www.forbes.com/councils/forbestechcouncil/2023/09/18/using-ai-in-cybersecurity-exploring-the-advantages-and-risks/

AI in Cyber Security: Pros and Cons, and What it Means for Your Business. (n.d.). https://www.terranovasecurity.com/blog/ai-in-cyber-security

Trinckes, J. (2024, August 14). How AI cybersecurity tools can protect your business in 2024. Thoropass. https://thoropass.com/blog/compliance/ai-cybersecurity-tools/

Community, D. (2024, May 13). The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions. Darktrace. https://darktrace.com/blog/the-state-of-ai-in-cybersecurity-the-impact-of-ai-on-cybersecurity-solutions

AI Overreliance Is a Problem. Are Explanations a Solution? (2023, March 13). Stanford HAI. https://hai.stanford.edu/news/ai-overreliance-problem-are-explanations-solution