Why security operations need an AI makeover

Modern SOCs are expected to deliver not just security, but also meet compliance, reporting, and training needs. With outdated security systems, security analysts face too many operational challenges. For instance, they’re always struggling to keep up with too many alerts. 

In cyber security, context is king. The fundamental idea of setting up any security operation is to have threat intelligence capable of thwarting risks. However, there’s information overload, false alerts, and lack of context – this purpose of reducing risks is defeated. Additionally, these alerts overwhelm security analysts. 
Analysts get thousands of alerts each day, here’s what a study² found on average:

• Number of alerts received daily: 4484
• Hours spent in the manual alert triage: 3
• Percentage of alerts handled/attended daily: 33%
• Number of false positives: 83%
• Time spent on alerts that aren’t a threat: 32%

Too many alerts and very few security analysts to manage them is a big challenge. The root of this problem is in the manual dependency. A study in 2023 highlighted that about 81% of SecOps professionals feel manual investigation slows them down. They’re required to initiate remediation actions manually.⁴ The same study also indicated that the average time to detect and respond has increased in the last two years. Analysts spend about one-third of their daily time investigating threats that aren’t real.

Growing manual dependency yet no respite in skill shortage

The heart of the issue is the shortage of security professionals to manage operations at scale. The adoption of digital and cloud technologies has gone remarkably high, increasing the attack surface. However, the number of skilled professionals in the security domain hasn’t improved in the last two decades. Forbes reported that in 2023 alone there were 3.5 million jobs in cyber security to be filled. It takes about 150 days on average to fill a security vacancy.⁵ Security professionals feel that their jobs are more difficult than they were two years ago. That’s because of growing complexities, the ever-increasing attack surface, the constant need to train and upgrade, budget pressures, and stress on meeting regulatory compliance.⁶

Traditional signature-based detection isn’t sufficient today because of its inability to detect new zero-day attacks and keep up with large volumes of malware. Artificial Intelligence can help the SecOps team to identify both known and unknown cyber risks, even in the absence of a signature. Artificial Intelligence uses machine learning (ML) algorithms to analyze a large amount of data to detect anomalies and patterns. Rule-based phishing detection is also a dated method as it cannot identify newer and unknown phishing emails. Whereas AI analyzes the email structure, content, and user interaction to flag a potential phishing attempt.

The same goes for log analysis, AI-based security log analysis can handle huge volumes in real-time as compared to rule-based systems. Not just external threats, but any internal threats such as unauthorized access or suspicious data transfer can be flagged by the AI.

Enhanced threat detection with AI

AI also makes network security very effective as its algorithms can monitor networks, detect unusual traffic patterns, identify unauthorized or suspicious devices on the network, etc.  Businesses can effectively prevent data breaches and security incidents with Artificial Intelligence. A key feature of AI is that it keeps learning and improving over time. As new cyber threats get into the market, AI models can learn from the new data to put up even stronger defenses. Businesses using MDR services that have AI-based endpoint protection experience faster and more effective responses from the security teams. To summarize, AI helps SecOps to improve efficiency, enhance real-time detection, add scalability, increase accuracy in decision-making, and more. 

With our full-fledged MDR services, we help businesses improve SecOps, lower security risks, and increase business resilience without affecting their digital transformation journey. Our MDR services use new-age AI-based technology like Palo Alto Networks’ CORTEX XSIAM. With AI capabilities, we can ingest data from more sources, correlate different alerts better, and lower the volume of high-priority alerts. This, in turn, brings down the resolution time from days to minutes. The unnecessary time spent on investigating low-priority or false positive alerts is eliminated.

Palo Alto Networks’ XSIAM capabilities:

1. No more switching of different consoles, all data is now available on a single console that integrates data from multiple sources.
2. Adding a new data source is also easy as compared to traditional SIEM – analyzing the data kicks in instantly.
3. Connect a range of products from the Cortex marketplace as needed.
4. XSIAM accelerates the investigation time by adding multiple alerts to a single incident. This saves a lot of time for analysts as they can focus on high-risk alerts.
5. With attack surface management (ASM), new vulnerabilities are quickly identified and shut down immediately.
6. XSIAM suggests response actions for analysts, thus saving time.
7. Threat detection and monitoring are also extended to new cloud systems that are added later by the organization offering enterprise-wide coverage.

T-Systems and Palo Alto Networks can augment your existing security operations without needing huge investments in security tools and having to rejig the entire SOC. Enhance your organization’s security posture and improve cyber resilience to defend against modern-day cyber-attacks.

With our AI-based MDR services, you can:

1. Reduce manual operations.
2. Scale up to prevent risks.
3. Reduce false positives.
4. Increase high-value alerts.
5. Improve investigation time.
6. Accelerate incident response.
7. Improve compliance and reporting.
8. Include visibility in real-time. 

To ramp up your security operations or benefit from our MDR services to build strong security for your business, get in touch with us now.