AI in cybersecurity: How does it impact organizations?

Artificial intelligence (AI) is a powerful weapon, and the impact it has on an organization depends on who wields it. While cyberattackers leverage AI to exacerbate existing vulnerabilities and carry out deepfakes and other AI-driven cyberattacks, cyberdefenders leverage AI to improve their organization's cybersecurity posture and threat detection and response capabilities. In this page, we will go over some of the ways in which both attackers and defenders use AI.

• Why is AI required in cybersecurity?
• Benefits of AI in cybersecurity operations
• How cyberattackers can use AI
• Conclusion

Why is AI required in cybersecurity?

With the influx of IoT devices in enterprise networks, migration of services and applications to the cloud, and integrations with several third parties, enterprise security has become complex. The surface area for cyberattacks has expanded, allowing attackers to exploit more loopholes in your network. This creates a need for defenses that can predict attacks at their earliest stages and nip them in the bud. Traditional security measures often struggle to keep up with the number of potential attacks and the sophistication of modern cybercriminals. However, AI has the ability to handle the increasing complexity and volume of cyberthreats faced by organizations.

Benefits of AI in cybersecurity operations

The benefits of AI in cybersecurity are manifold. The bright side of artificial intelligence includes providing insights to predict cyberthreats, perform routine security procedures, and simplify enterprise security. Here are some solid benefits to having AI-infused cybersecurity:

• Improved threat detection and prevention: AI can identify and mitigate potential threats before they can inflict damage. Traditional cybersecurity measures often rely on known signatures to detect malware and viruses. However, AI can analyze vast amounts of data and identify patterns indicative of malicious activity, even when no prior signature exists. Cybersecurity solutions like SIEM have a UEBA capability built into them. UEBA makes use of machine learning (ML) algorithms, which are a subset of AI, to learn from historical data to recognize or predict new threats, including zero-day attacks. This is called anomaly detection because it can identify deviations from normal events and alert you about outlier events. Such predictive analytics can help security teams stop attacks preemptively.
• Extended scope for threat intelligence: AI can detect and analyze fragments of malicious code, learn from these fragments, and store this information to detect similar threats down the line. It can also process information from dark web forums, malware databases, and real-time attack data to generate actionable intelligence. AI can also build threat forecasts based on new developments in the cybersecurity sphere and be used to develop anticipatory intelligence by training it on historical data. This can help organizations stay ahead of emerging threats and adjust their defense strategies accordingly.

  For example, AI can be trained to learn malicious IPv4 addresses. It can then predict IP addresses that have a close resemblance to known malicious IPs and identify them as addresses belonging to threat actors. This helps SOC analysts block potential malicious IP addresses before any intrusion takes place.

• Reducing false positives: Traditional measures, including rule-based attack detection, often generate numerous false alerts, overwhelming security teams. This is because the threshold they set for the alert to be triggered might be too low or too high. But, AI and machine learning improve the accuracy of threat detection by reducing false positives. For instance, a SIEM solution like ManageEngine Log360 offers smart thresholds, where the ML model analyzes your network behavior to arrive at a threshold and makes adjustments based on emerging new patterns. Log360 also offers factors such as peer group analysis, seasonality, anomaly modeling, user identity mapping, and custom risk scoring to improve the accuracy of user risk scores. This will help in ensuring that security professionals focus only on genuine threats and not on fatigue-inducing false positive alerts. You can learn more about Log360's smart thresholds here.
• Enhanced incident response: AI can help build a comprehensive context for security incidents. Seemingly random events across your network can sometimes be related. AI can determine if these individual events have a connection that indicates a serious security incident. This helps improve how security alerts are prioritized, leading to faster responses and better threat mitigation. Moreover, when a potential threat is detected, AI-driven cybersecurity solutions can automatically initiate predefined response protocols, such as isolating affected systems, blocking malicious IP addresses, shutting down unauthorized access points, or initiating security protocols. This swift reaction is crucial in minimizing the impact of cyberattacks, protecting sensitive data, and enhancing overall security resilience.
• Automation of routine security tasks: Human effort in cybersecurity should be used for innovative tasks like building strategic defences for the network. Meanwhile, AI solutions can perform more routine tasks like security incident responses and management. Root cause analysis of security incidents can also be automated using an AI-based cybersecurity solution to improve the incident response time.
• Breach risk prediction: Taking into account your IT assets, the security measures in place, and your company's data exposure to third parties, AI can predict the risk of your organization being breached. AI solutions can also identify weak spots in the network that need more attention, helping you plan your security strategy accordingly. Moreover, AI's adaptive learning capability can help organizations stay ahead of cybercriminals who are constantly evolving their attack techniques and tactics to inflict maximum damage.
• Creating an asset inventory: AI can help you acquire a full inventory of all your network assets like servers, devices, and users. It can also help you categorize these assets by how critical they are to your business operations.

  For example, AI can classify important servers with sensitive information as high priority. This helps security operations center (SOC) analysts better understand the network architecture and implement more stringent security for high-priority assets. This reduces the overall risk of exposure and not only saves the company monetary losses but spares them from legal consequences in the long run as well.

While AI and ML offer great benefits to cyberdefense, they also present significant security risks and challenges. Attackers can leverage AI to compound existing vulnerabilities and carry out cyberattacks.

How cyberattackers can use AI

The dark side of AI in cybersecurity includes the potential for AI-driven attacks, deepfakes, the exacerbation of existing security vulnerabilities, and the risk of escalating cyber warfare. Understanding these risks is essential for developing strategies to mitigate the negative impacts of AI in cybersecurity.

AI-driven cyberattacks: AI can be used by malicious threat actors to carry out sophisticated cyberattacks that are harder to detect than conventional attacks. For example, AI can create highly convincing phishing emails by mimicking writing styles and personalizing messages based on harvested data. But that's not the only thing it can do. AI can generate polymorphic malware that changes its code to evade detection by conventional security measures. Moreover, attackers can get their hands on AI-based cybersecurity solutions to test their malware. This helps them create a strain of malware that is resistant to AI's protective measures. Attackers can also leverage AI to enhance the coordination and efficiency of botnets, making DDoS attacks more sinister. Moreover, AI has the potential to escalate cyber warfare by increasing the scale and speed of attacks. Autonomous AI systems can conduct operations without human intervention. This, when combined with misinterpreted data or faulty decision-making algorithms, can rapidly escalate the risk and degree of cyber conflicts.

Deepfakes: Deepfakes are images and video that use AI's deep learning technology to create fake media—for example, a fake video of a CEO sharing false information about a company. Deepfakes can replace the face of a random person in a video with the face of a company CEO. This could have dire consequences for the company. You can learn more about deepfake technology here.

Exacerbating security vulnerabilities: AI can exploit existing vulnerabilities and lead to new security issues. To accomplish this, attackers can execute adversarial attacks, which involves manipulating input data to deceive AI models. The act of corrupting the data used to train AI models, which results in flawed and potentially harmful outputs, is called data poisoning. By exploiting the way in which AI interprets data, attackers can manipulate AI into making incorrect decisions. For instance, adversarial inputs can trick image recognition systems into misidentifying objects.

Conclusion

Combating AI-driven potential threats from attackers will already be a huge task for cyberdefenders, but that's not the end of it. They have one more concern to deal with, and that's the privacy issue concerning the use of AI.

AI requires a huge amount of data to train effectively. The large datasets used for training AI models can be attractive targets for cybercriminals. A breach can result in the exposure of sensitive, personal, and confidential data. Moreover, AI can perpetuate existing biases if they are trained on biased data, and since its decision-making process is opaque, it's difficult to understand how security decisions are made, thereby affecting transparency and accountability.

Addressing the security issues surrounding the use of AI requires a multi-faceted approach, including proper security measures, ethical guidelines, continuous monitoring, and the integration of human expertise with AI capabilities. By acknowledging and mitigating these risks, organizations can harness the benefits of AI in cybersecurity while minimizing its potential dangers. So, if your organization's security budget and human resources can take on AI-based cybersecurity solutions at this point, we strongly recommend doing so because it will be a good long-term investment. When attackers are leveraging AI, so should organizations. With a SIEM solution like ManageEngine Log360, you can leverage ML-based security alerting and threat detection to thwart both insider threats and external attacks. To learn more, sign up for a personalized demo and talk to our product experts.