Why Enterprises Should Consider AIOps in Cyber Security

Integrating AIOps (Artificial Intelligence for IT operations) in cyber security is becoming essential as cyberattacks become more sophisticated. It helps firms position themselves at the forefront of defense, ready to tackle emerging threats with agility and precision.

AIOps blends Machine Learning (ML) and big data analytics to automate and improve IT operations. These can also include cyber security operations.

As per Statista’s report, Value of the AI cybersecurity market worldwide –

Here are why firms should consider adding AIOps in cybersecurity frameworks.

 Benefits of AIOps in Cybersecurity

1. Automated Threat Detection

AIOps solutions use machine learning algorithms for analyzing large datasets encompassing network behavior and security-related information. This helps to find security threats early and prevent malicious activity in an IT system.

As per Statista’s report, Top benefits of incorporating AI into cyber security operations –

ML allows the system to learn from data, identify patterns, and make decisions with minimal human intervention.

AIOps detect irregular or harmful activities that do not fit the usual patterns. However, human analysts cannot identify threats due to the size and complexity of the data.

Enterprises need larger security operations set up, and human operators cannot contribute there. Clearly, AI based analytics tools only can analyze this volume of data, and at a faster pace.

For instance, various incidents can happen daily at a large firm. This might range from routine data transfers to harmful trespassing. Manually examining each event is both impractical and inefficient. Detecting and resolving threats promptly is important to mitigate its impact.

This way, security teams can solve potential security issues before they escalate. This reduces the “window of opportunity” for attackers, limiting the damage they can inflict.

Also read: Why AIOps is Crucial for Continued Operations and Future Success

2. Enhanced Incident Response

In incident response, AIOps can handle routine and repetitive tasks. This frees human IT professionals to focus on tasks requiring critical thinking. They can instead focus on strategic decision-making, and creative problem-solving.

Discussed below are ways AIOps automate certain aspects of the incident response process:

• Automated Initial Analysis

When an incident occurs, security teams spend a lot of time on the initial analysis. This analysis involves understanding the nature of the incident and its potential impact.

AIOps automates this initial screening process. It can quickly analyze data using algorithms and ML, which helps to determine the nature of the incident.

Fast inspection allows for a quicker understanding of the situation. This speeds up the entire response process.

• Automated Triage

Triage refers to determining the order of urgency based on their severity, urgency, and impact on the business. AIOps automates this process by setting predefined criteria for classifying incidents.

As soon as an incident occurs, the system understands its priority level. Accordingly, it can address the high-impact issues first. This optimizes the use of available resources.

• Implementing Containment Measures

In certain cases, AIOps can help to identify the threats extremely fast. They can complete the initial analysis and enforce more efficient containment measures.

For instance, if a company faces a cyber security threat, the system can automatically isolate affected systems or devices. It can thus control the risk, stopping it from spreading further. This measure greatly reduces damage.

Also, if the tools identify these issues faster, the security teams have more time to understand the risks. They can then implement better strategies to resolve and control these security threats.

3. Enabling Predictive Analytics (PA)

PA uses AI and ML to examine historical and current data and predict future events.

AIOps uses AI and ML tools to identify patterns and anomalies. They can also detect possible threats that human analysts may take time to determine.

This ability to predict future security threats and risks allows firms to take proactive measures.

Let us take the example of a firm that has faced multiple breach attempts in the past. AIOps can identify a pattern in breach attempts targeting a specific risk. It can also ascertain that similar attempts may occur in the future.

This helps firms address the issue before possible breaches, boosting their security.

This proactive approach differs greatly from traditional reactive security measures. Earlier, the focus was on responding to threats after they happened.

Anticipating threats allows firms to implement preventative measures. This reduces the impact of cyber-attacks and streamlines their overall IT operations.

4. Reduced False Positives

Traditional security tools constantly monitor for potential issues. The potential alarms can range from unauthorized access attempts to malware infections.

But sometimes, if the tools are too sensitive, they can trigger alarms for actions that may be harmless or routine.

This results in many alerts, many of which may be false positives. They are alerts that identify actions as suspicious or malicious, even though they are benign.

For IT teams, reviewing this high volume of alerts is time-consuming. They have to go through each alert to decide if it’s actually a threat. This diverts attention from potential real threats, increasing the risk of a notable oversight.

Using ML and data analytics allows AIOps to manage these alerts effectively. It looks at the broader picture and considers the system’s operational data. This means it doesn’t look at isolated alerts. It’s then about seeing how each of the incidents, which could be system updates or system access, fits the real threat pattern.

This improves the efficiency of security operations and ensures vital alerts receive the immediate attention they require.

5. Automates Compliance Monitoring and Reporting

AIOps tools automate the monitoring and reporting processes related to compliance. This means they can run continuous scans and examine the IT environment. This ensures that all operations follow necessary regulatory standards like GDPR or HIPAA.

Automating these tasks allows firms to maintain compliance without devoting much manual labor to these tasks.

Conclusion

Cyber security is no longer just about defending against known threats. It is about being proactive, predictive, and efficient. Firms are facing increasing pressure to improve their cyber security operations.

AI tools could be the best bet for security leaders and teams. they enable much less human skills requirement. With less resources, they can deliver a much higher level of accuracy for identifying security risks. They can work through huge amounts of data and identify risk patterns much faster.