Alleviating Alert Fatigue: 3 Ways AI Does It
In the rapidly evolving landscape of cybersecurity, several challenges can strain even the most seasoned cybersecurity professionals. Among these challenges is "alert fatigue," a byproduct of traditional security systems inundating Security Operations Center (SOC) teams with countless alerts—many of which are false positives. The advent and maturation of AI technology, however, promises a paradigm shift in how we manage these overwhelming notifications. In this blog post, we explore three pivotal ways AI alleviates alert fatigue, enhancing efficiency and effectiveness for SOC teams.
1. Predictive AI for Proactive Threat Mitigation
Predictive AI is a critical asset in the fight against cyberthreats. Instead of reacting to breaches, predictive AI empowers businesses to anticipate and prevent them. How does it work? By analyzing vast amounts of historical and real-time data, predictive AI can identify patterns that indicate potential threats within your environment and dismiss those that do not. This allows you to focus on genuine alerts rather than sorting through an avalanche of false positives.
Predictive AI’s strength lies in its ability to pull in context from multiple data points—such as the origin of a suspicious file, its behavior across networks, and its interaction with endpoints. By connecting these dots, predictive AI can accurately determine the likelihood of an attack, so your organization can allocate resources more efficiently.
You can find a prime example of predictive-AI success in CylanceENDPOINT™, which is highly rated and proven to block between 98.9% and 100% of threats by predicting malware behavior. This predictive power includes zero-day threats both now and, amazingly, into the future.
Shiladitya Sircar, who is Senior Vice President of Product Engineering & Data Science at BlackBerry, recently wrote about how this is possible.
“Given that we cannot fast-forward or rewind time, we train the models using malware classes from the past, and test them against newer malware from the present. The goal of this temporal (time related) testing is to validate generalized performance over time, which is crucial for detecting zero-day threats.”
Predictive AI in Cybersecurity: Frequent Update Pitfalls
Predictive AI does great things, however, there is a specific pitfall to watch for. Many EPPs (endpoint protection platforms) are cloud dependent and must run frequent updates to stay fully effective.
“It is reasonable to ask the question,” says Sircar, “Why does the updates piece matter? After all, models can be updated frequently in the cloud, which is typically where most models are served from. However, there are many endpoints — such as in IoT, regulated industries, or disconnected and even intentionally air-gapped endpoints — that are not cloud-connected. Updating models may not always be feasible in these cases. In models that are heavily cloud-dependent, a loss of connectivity can greatly decrease detection rates.”
In addition, the more frequently a solution pushes out updates, the greater the odds they may cause a disruption, like the 2024 global IT outage.
This means that when considering which predictive-AI solution to use, look at third-party analysis to see how “predictive” the solution truly is, even when it’s disconnected from the cloud. Predictive AI reduces alert fatigue, but the specific tool you choose will determine how great that reduction is.
2. Generative AI Enhances Investigation Processes
Now, let’s look at another type of AI that helps reduce alert fatigue for cyberdefenders. Generative AI complements predictive AI by streamlining investigation processes and reducing unnecessary escalations. It does so by generating comprehensive reports and summaries from large datasets, effectively tackling alert fatigue.
With generative AI, your team can quickly gather all relevant information about a potential threat, bypassing the time-consuming process of manually collating data from disparate sources.
One example of this is Cylance® Assistant from BlackBerry, which employs generative AI to provide your SOC team with contextual guidance directly within your operational workflows. This ensures that analysts have immediate access to the insights needed to assess threats accurately.
The single console approach of Cylance Assistant also minimizes the need for context switching. By enabling security analysts to make faster and more informed decisions, generative AI not only expedites threat resolution but also upskills team members, empowering them to handle more complex situations and reducing the number of escalations to senior members of the team.
Generative AI in Cybersecurity: Watch for “Prompt Creation” Pitfalls
One complaint we have heard about generative AI in cybersecurity tools is around the work that goes into creating AI prompts. This is time consuming and if you’re asking the wrong questions — it can negatively impact your outcomes.
That’s why the CylanceENDPOINT team built Cylance Assistant to be “context aware.” You never need to create prompts because it actually knows the context of where you are in your investigation, and instantly tells you what you need to know, with a single mouse click. To see it in action, watch this brief video for yourself:
3. AI-Driven Human-Machine Collaboration
The combination of predictive and generative AI in cybersecurity ecosystems creates a potent force multiplier to speed investigations and reduce the number of them in the first place. However, the most effective approach involves integrating AI with human expertise. Human analysts excel in interpreting nuanced information and making judgments that machines cannot.
This synergy between AI and human judgment is crucial for managing alert fatigue. While AI sifts through and organizes alerts, human analysts can focus on the qualitative aspects of threat assessment. This collaboration not only optimizes the workflow within your SOC team but also significantly reduces the time taken to respond to actual threats.
To maximize this potential, an increasing number of organizations are turning to AI-powered MDR (managed detection and response) services, like CylanceMDR™. Listen to BlackBerry VP and Advisory CISO Gregory Richardson explain how this is the sweet spot of uniting artificial intelligence alongside human intelligence.
Conclusion
The integration of AI into cybersecurity is not about replacing human intelligence but augmenting it. By leveraging predictive and generative AI, you can dramatically reduce alert fatigue and empower your SOC team to operate with greater efficiency and precision. The partnership between AI and human expertise sets the stage for a future where cyberthreats are not just managed but anticipated and neutralized before they manifest.
Ready to see how AI can transform your security operations? Have a conversation with us or learn more, here.
