AI and Cybersecurity: Shaping the Future of Threat Defense

Cybersecurity has become a main concern for individuals and organizations alike, as threats are getting more and more advanced by the day. Artificial Intelligence (AI) has emerged as a game-changer in the realm of cybersecurity, offering powerful tools and techniques to improve our defenses and stay one step ahead of cybercriminals.

Below we explore how AI is being used to improve cybersecurity and the benefits it can offer to organizations. Additionally, we provide recommendations for companies looking to implement AI-based cybersecurity solutions.

The growing cybersecurity challenge

Cybersecurity threats have evolved from simple viruses and malware to complex, multi-faceted attacks that exploit network, software, and human behavior vulnerabilities. Hackers are now armed with advanced tools and techniques, such as ransomware, zero-day exploits, and social engineering tactics, making it increasingly difficult to detect and stop their activities. Thus, it’s not surprising that the global cybersecurity market is expected to grow from $296.1 billion in 2022 to $538.3 billion by 2030.

While still essential, traditional measures are often reactive in nature, relying on signature-based approaches and rule-based systems to identify known threats. This approach leaves organizations vulnerable to new, unknown threats and zero-day vulnerabilities. This is where AI steps in as a proactive and dynamic solution.

Benefits of using AI for cybersecurity

According to a report by Gartner, 80% of security solutions are expected to use AI and Machine Learning (ML) by 2025. Some of the benefits they can bring to organizations include:

Reduce costs

AI can help organizations save a significant amount of money by reducing the risk and cost of data breaches. Based on findings in IBM's 2023 cybersecurity report, companies can save around $1.76M only from using AI to prevent data breaches. Additionally, the report found that the global average cost of a data breach reached 4.45 million USD in 2023, a 15% increase over the last 3 years. Beyond direct financial savings, AI also minimizes operational disruptions, enabling businesses to maintain continuous operations even during attempted breaches, which can save further costs associated with downtime.

Provide insights into security threats and vulnerabilities and help make better decisions

AI-powered security analytics tools can provide organizations with insights into the threats they face and the vulnerabilities in their systems and networks. For example, the Splunk Security Intelligence Platform uses AI to analyze security logs and other data to identify threats and vulnerabilities. By leveraging these insights, security teams can prioritize the most critical issues and allocate resources more effectively, leading to faster decision-making and enhanced overall security posture.

Adapt to new cyber threats more quickly than humans

AI solutions can analyze large amounts of data from a variety of sources (such as security logs and network traffic) to identify patterns of malicious activity. Once the algorithms are trained, they can be used to detect new threats even if they have never been seen before. The Darktrace AI platform, for instance, uses a similar approach, allowing it to detect threats that traditional signature-based security solutions would miss. This ability to adapt in real time ensures that organizations are always protected against the latest threats, reducing the window of vulnerability and preventing potential breaches.

Identify and address vulnerabilities more effectively

AI-powered scanners can scan systems and networks for vulnerabilities more effectively than traditional methods. This is because they can learn and adapt to new threats more quickly and scan systems and networks at a much larger scale. For example, the Qualys Cloud Platform utilizes AI to scan systems and networks for vulnerabilities in real time, which helps identify weaknesses before attackers can exploit them. Additionally, AI-driven vulnerability management can automate the remediation process, reducing the time it takes to patch critical flaws. This improvement enhances the overall security lifecycle management.

Applications of AI for cybersecurity

Here are some of the ways AI is being used in cybersecurity today:

AI-powered threat detection

AI is transforming cybersecurity threat detection by enabling real-time analysis of vast amounts of data. This allows for the identification of anomalous patterns and potential threats that may go unnoticed by human operators. ML algorithms can recognize subtle deviations from normal behavior, enabling early detection of malicious activities.

AI can also improve malware recognition by using behavioral analysis to identify suspicious code execution and network behavior, even when dealing with previously unseen malware strains. This proactive approach to threat detection is a significant advantage in the battle against cybercriminals.

Read more: Penetration testing for financial institutions whitepaper

Behavioral analysis and user anomaly detection

Human error remains a significant weak point in cybersecurity. Employees inadvertently clicking on phishing emails or falling victim to social engineering attacks can lead to data breaches. In 2022 the Association of Certified Fraud Examiners conducted a report using data from 2,110 actual fraud cases that were examined in 133 different countries. The results showed total losses of $3.6 billion and an average loss of $1.78 million per case. Fortunately, AI can mitigate this risk through user anomaly detection.

AI systems can establish a baseline of normal user behavior for everyone within an organization. When deviations from this baseline occur, such as unusual login times or access requests, AI algorithms can trigger alerts, potentially preventing insider threats and unauthorized access.

Here are some examples of how behavioral analysis and user anomaly detection are being used today:

·      Detecting Insider Threats: Insider threats involve malicious actions by individuals who have authorized access to an organization's systems and data. A study by the Ponemon Institute found that 60% of organizations have experienced an insider threat incident in the past year. To combat that, behavioral analysis can identify insider threats by monitoring user activity for unusual patterns, such as accessing sensitive data at unusual times, downloading large amounts of data, or making unauthorized changes to systems.

·      Identifying Account Compromise: Account compromise occurs when an attacker gains unauthorized access to a user's account. User anomaly detection can identify account compromise by monitoring login activity for unusual patterns, such as logins from unfamiliar locations or multiple failed login attempts.

·      Detecting Fraudulent Activities: Fraudulent activities involve deception to gain something of value. Behavioral analysis can detect fraud by monitoring user behavior for unusual patterns, such as making unusual purchases, accessing unusual accounts, or attempting unauthorized transactions.

Accedia user anomaly detection case study

At Accedia we’ve come across several cases proving how powerful such tools can be. For example, in a manufacturing process where rare earth materials are processed, the outputs produced were not consistent. Therefore, our data scientists decided to apply data mining techniques. The results were impressive - for specific shifts with specific employees, machines were being stopped for “urgent maintenance due to malfunction”. Further analysis showed that the output materials were consistently less than other shifts (2 grams less power material), accounting for the downtime. An internal investigation, backed by data analysis, revealed that there was an organized group of employees stopping machines and stealing from the products, with a sophisticated clandestine export process.

Predictive analysis and threat intelligence

Predictive analysis is the use of historical data to predict future events. In the case of cybersecurity, it can be used to predict future cyber-attacks.

On the other hand, threat intelligence offers information about current and emerging cyber threats. It can be collected from a variety of sources, including security vendors, government agencies, and open-source information.

Predictive analysis and threat intelligence can be used together to improve the effectiveness of cybersecurity programs. The first one can identify potential targets and vulnerabilities, while the second one - specific threats and attack vectors.

Conclusion

AI is revolutionizing cybersecurity by providing advanced threat detection, proactive defense, and automated incident response capabilities. Its ability to analyze vast datasets and adapt to evolving threats makes it an indispensable tool in the fight against cybercriminals. As organizations increasingly embrace AI-driven cybersecurity solutions, they will be better equipped to protect their data, systems, and reputation.

Overall, the future of AI for cybersecurity is very promising. AI is expected to play an increasingly important role in helping organizations to protect themselves from cyber-attacks.

Learn more about the future of AI for cybersecurity by reading our full whitepaper on the topic!