Today’s businesses aren’t just protecting themselves against human attackers anymore. Artificial intelligence (AI) can enable criminals who may lack the technical acumen to carry out a cyberattack on their own by providing “as-a-service” tools to more users, expanding the threat landscape and making global ransomware and other cybersecurity threats more prevalent.
To meet and overcome these threats, security teams should consider implementing AI tools as part of their cloud computing environment to counteract cybercrime and improve their cybersecurity posture.
We’ll cover the role of cybersecurity, why traditional tools won’t cut it anymore, and some use cases security professionals might consider when evaluating potential applications of these tools.
Why Traditional Tools Are Not Enough in Today’s Cybersecurity Landscape
As cyberattacks become more sophisticated, security analysts face growing challenges in preventing and resolving these incidents effectively. Compounding this issue is an overwhelming number of alerts about attack indicators, which can lead to alert fatigue, hindering the ability to detect and respond to genuine threats promptly. Additionally, the need to operate multiple traditional tools demands constant vigilance, often impeding effective remediation efforts.
This combination of factors – the intricacy of cyberattacks, the overwhelming volume of alerts, and the limitations of traditional tools – creates a large obstacle for organizations striving to maintain strong cybersecurity postures
Understanding the Role of AI in Cybersecurity
While traditional cybersecurity tools can struggle to keep pace with the changing face of incoming threats, AI can influence security and offer a new line of defense that protects your critical data and systems. When trained effectively, AI tools have many use cases, including monitoring, detecting, and predicting threats with greater accuracy and efficiency. AI can also make it easier to manage security patches, reduce the incidence of alert fatigue, and allow businesses to respond faster to legitimate incidents. Because AI tools can evolve alongside the threat landscape, they can also improve response capabilities over time.
3 Benefits of Using AI in Cybersecurity
AI provides innovative solutions that can significantly enhance defensive capabilities. Three of the benefits of using AI in cybersecurity include enhanced threat detection, automated processes, and continuous learning.
Enhanced Threat Detection
AI algorithms can surpass human limitations in processing, allowing for analysis of large amounts of security data, user activity, network logs, and other system events. This data analysis can pinpoint anomalies or patterns that might go unnoticed by human observation.
AI learns from historical data and pulls in information from threat intelligence feeds to proactively identify potential security incidents. This helps security teams identify indicators of attack before they lead to a breach.
Unlike humans, AI tools work tirelessly around the clock to monitor for anomalies and malicious activity which can assist security leaders in detecting and responding to threats quickly.
Process Automation
Instead of having to sort through each security alert and experiencing alert fatigue, AI can take care of the bulk of analysis, filtering out false positives and pushing more urgent alerts to security teams for further investigation. This can improve accuracy and efficiency, while giving security teams more time back to take care of more high-level, strategic tasks.
AI can also automate processes that respond to a security breach. Containment, remediation, and recovery efforts can all be automated, reducing the amount of time spent before addressing an event, as well as mitigating the spread to other parts of your environment.
Applying security patches in a timely manner is critical. These often reflect known vulnerabilities that bad actors can use to exploit your systems. By automating the implementation of security patches to your systems, you can boost your security posture with little to no additional effort beyond the initial rule configuration.
Continuous Learning
Cybersecurity tools rely on human intelligence to function properly, especially AI tools that require training to adapt to new and changing threats. However, these tools can also help humans stay one step ahead of attackers, adapting to changes automatically through a continuous learning process. Security teams can optimize their defenses with AI self-tuning security parameters, and AI tools can also be used to share threat intelligence data with other organizations to improve the security of many other businesses.
3 Challenges of Using AI in Cybersecurity
Any new initiatives or technologies come with their own challenges. When it comes to AI tools, businesses should be mindful of domain adaptation, concept drift, problems with unlabeled data, and issues related to reasoning and transparency behind certain security decisions.
Domain Adaptation and Concept Drift
Models can become obsolete if they are trained with outdated data or limited data sets. This can mean teams will need to engage in regular retraining and adaptation to meet the challenges of a dynamic threat landscape. The more data you can use to train models, and the more recent the data, the more effective the tools will be.
Lack of Labeled Data
Supervised machine learning models depend on labeled data for training. Cybersecurity data may not be labeled, which makes it challenging to apply traditional supervised learning techniques effectively. Instead, models are often trained on unsupervised methods, such as anomaly detection. This can generate false positives and require more upfront work to refine before alert fatigue is eliminated.
Reasoning and Transparency
When it is trained on accurate, current data, especially if it is labeled, AI can be highly effective at identifying threats. However, transparency can be an issue. It can be difficult to understand the reasoning behind AI’s decision sometimes. This speaks to the importance of implementing solutions alongside AI tools that can explain why certain activities have been flagged.
Applications of AI in Cybersecurity
Security professionals can improve the way they approach threats by using AI in a variety of ways. Below, find a few applications you may want to consider for your team.
Predictive Security
Proactive threat hunting using AI algorithms can predict security incidents before they happen through the analysis of large amounts of security data. When teams are able to take preventative measures, they can significantly reduce the risk posed by software vulnerabilities and other attack vectors.
AI can also be used to predict what attacks may look like, uncover weaknesses and place priority on certain areas where stronger defenses are needed. Simulating a cyberattack can help your organization see what could pose a problem in the future.
Intelligent Threat Detection and Response
Intelligent threat detection and response (TDAR) is an advanced form of pattern recognition used by AI that can find subtle changes in user behavior, system, activity, and network traffic that may not be noticeable to humans but could be indicative of malicious intent. This can be paired with automated incident response measures, limiting the damage and giving teams a head start on attackers.
Vulnerability Management and Risk Assessment
Critical vulnerabilities should be addressed before less important matters, and AI can help teams sift through everything to prioritize the most important patches and mitigation efforts first. This can be done, in part, through risk scoring, which AI can conduct by analyzing a few different factors to assign risk to each vulnerability.
User and Entity Behavior Analytics
Inside threats or compromised accounts can cause major issues for organizations. Insider threat detection can identify suspicious, anomalous activity by first understanding what normal user behavior looks like. If a user attempts to download sensitive files, access unauthorized data, or violate established security policies, this can be a sign of an insider threat.
Account takeover can be another big issue. Compromised credentials were the second-largest attack vector for data breaches in 2023. Account takeover prevention can include monitoring for anomalies in user login attempts and preventing hackers’ ability to steal legitimate user accounts to gain access to system data.
Network Security and Malware Analysis
Intrusion detection and prevention systems powered by AI can look at network traffic in real-time. From there, tools can be used to prevent malicious activity, such as unauthorized access attempts, data exfiltration, and malware attacks.
Malware threats can also be analyzed using AI, which can be trained to spot new and emerging malware. This allows security terms to get ahead of cyber attackers and form more effective defenses against potential threats.
Embracing AI as a Strategic Imperative
Proactive security measures can greatly improve your organization’s ability to reduce the risks associated with cybersecurity threats. AI is no longer a hypothetical – consider it a strategic imperative to better protect against cyberattacks. If you’re interested in learning more about how you can incorporate AI into your cybersecurity plans, contact the specialists at TierPoint today.
The business applications for artificial intelligence and machine learning are still taking shape. Learn about some of the most popular applications in our white paper.
