GUIDE

It is just another night shift for a security analyst when he receives an alert about a user who is trying to log in from a new location.

Thinking, “it is just another alert,” he ignores it. After some days, the company reports a data breach.

What happened here? Let us find out!

Why traditional SOC doesn’t work for modern needs?

In the above scenario, the security system failed to flag what it should have:

• The login was from a place where the user cannot travel to in any scenario
• The user who logged in accessed data only minutes later
• An attempt to laterally move across the network followed the login

The attacker dwelled in the network for days until the breach was discovered. It is not a rare thing but a modern cyber attack scenario faced by most organizations today.

It took 284 days on average for organizations to identify and contain a breach in 2025. (IBM Cost of Data Breach Report 2025). The average cost of data breach was $4.4 million. It means that breaches didn’t just take more time but were also costlier.

Why? Because attackers dwell for longer and cause more damage.

The real problem for organizations isn’t no detections but detections without any accuracy.

Traditional SOC worked around the idea that threats are manual, slow, and predictable.

This is why they are failing to predict modern AI-driven threats that are: – automated & adaptive, using phishing that is hyper-personal, indistinguishable, and malware that evades signature-based detections in minutes.

It is why they are failing to predict modern AI-driven threats

On top of this, organizations are also dealing with challenges like expanding cloud/hybrid setups with disconnected tools, limited visibility & teams (with the growing global cybersecurity workforce gap of 19% YoY), and growing security alerts (millions every day).

The real problem is that security analysts face thousands of alerts every day, out of which almost half are false positives. They are losing focus on real alerts that are missed.

What does accuracy mean in SOC?

Accuracy in SOC is often misunderstood as “detecting more threats”.

In reality, it is about detecting genuine threats that demand immediate attention and reducing all the unnecessary noise (false positives), and accurately responding through clear context.

SOC accuracy can be broken into three categories: true positives (correctly identified real threats), false positives (non-harmful activities flagged as threats), and false negatives (real threats that aren’t flagged as threats). SOC teams use metrics like false positive rate (FPR), false negative rate (FNR), MTTD (Mean Time to Detect), and MTTR (Mean Time to Respond).

Accuracy cannot be aced through a single metric but a balance of every metric to achieve accurate detection while reducing unnecessary noise. With limited context and static rules, the traditional SOC approach often fails to achieve this balance.

Modern AI-driven SOC solves this by detecting what matters, instead of detecting more.

What does accuracy mean in SOC?

Accuracy in SOC is often misunderstood as “detecting more threats”.

In reality, it is about detecting genuine threats that demand immediate attention and reducing all the unnecessary noise (false positives), and accurately responding through clear context.

SOC accuracy can be broken into three categories: true positives (correctly identified real threats), false positives (non-harmful activities flagged as threats), and false negatives (real threats that aren’t flagged as threats). SOC teams use metrics like false positive rate (FPR), false negative rate (FNR), MTTD (Mean Time to Detect), and MTTR (Mean Time to Respond).

Accuracy cannot be aced through a single metric but a balance of every metric to achieve accurate detection while reducing unnecessary noise. With limited context and static rules, the traditional SOC approach often fails to achieve this balance.

Modern AI-driven SOC solves this by detecting what matters, instead of detecting more.

How does AI improve SOC’s detection accuracy?

AI makes way for clarity among too many alerts, limited context, and little time through noise reduction, improved correlation, and accurate detection. 

Catches abnormal behavior instead of “known bad”

Traditional SOC relied on the detection of threats based on signatures and known patterns. However, AI-driven SOC checks for deviations in the behavior. It builds behavioral baselines based on things like – when do users usually log in?, what are the systems they log into, and the location from which they typically access the system. It can help proactively prevent zero-day attacks, insider threats, and credential misuse.

Bring incident-related context from mere alerts

AI helps security teams to analyze what matters by reducing noise through correlation of signals across endpoint activity, identity systems, email behavior, and network traffic. This helps offer context that traditional SOC with isolated analysis of alerts doesn’t. For example, an instance of failed login doesn’t say anything, but when combined with context like unusual location or escalation of privileges, it gives a broader context for action and makes way for precision in response.

Helps draw a complete picture of risk across infrastructure

Based on factors like deviation from baseline behaviors, criticality of assets, and threat intelligence, AI assigns risk scores. These scores are given to all the users, devices, and activities, making it easier for the SOC team to prioritize highly risky incidents and prevent time wastage on low-priority ones. It helps shift attention to where it matters, improving the overall accuracy.

Detects evolved threats

One of the biggest challenges is that signature-based defenses don’t work against polymorphic and fileless attacks. Modern-day threats move past traditional defenses that detect based on known signatures and patterns. AI models can help security teams tune AI models to detect suspicious process execution patterns, signs of memory-based attacks, and behaviors reflecting lateral movement. It helps prevent false negatives that can cause security chaos.

Adaptive through continuous learning

A defense built with AI continuously evolves based on new data, like new threat intelligence feeds, news, etc. It adapts to the changes in the threat landscape and improves detection with attacker techniques, so instead of being surprised by a new technique, the defense is already prepared with a proactive response.

Real world use cases of AI driven SOC

The following are some of the real-world use cases of AI-driven SOC

• Early discovery of compromise

AI can help detect anomalies even without malicious IPs, like suspicious location logins, unusual time of login, and access from new devices.

• Identifying insider threats

By forming a behavioral baseline, AI can help detect unusual patterns related to data access, any large file transfer, or access outside job roles.

• Blocking lateral movement

Through the detection of behaviors like abnormal authentication requests, suspicious remote execution, and privilege escalation patterns, it prevents lateral movements.

• Detecting advanced phishing attacks

By analyzing language patterns, sender behavior, and user interaction signals, AI can help identify phishing emails that have even bypassed traditional filters.

• Threat intelligence

AI can compile and correlate data from multiple sources, helping security teams analyze and act with multi-sourced threat intelligence.

• Vulnerability management

Security teams can automate the process of scanning, categorizing, and prioritizing vulnerabilities for patching.

Limitations of AI-driven SOC

While AI provides a range of possibilities for SOC, it comes with its own set of limitations that you should be aware of. These include:

• AI models are based on data they are fed – poor data means inaccuracies in models.
• Model drift – Without timely retraining, AI models can become ineffective.
• AI attacks – attackers may manipulate AI systems to camouflage their presence or evade detection.
• Can make decision-making challenging – you still need reliable human expertise for accurate investigation and decision-making.

AI cannot replace human expertise, but it can turbocharge productivity. Therefore, the most effective SOC combines the capability of AI and HI (Human Intelligence).