How insight, AI and collaboration are building the proactive SOC

Earlier this year, a report revealed 70 percent of security operations centre (SOC) agents were suffering from “alert fatigue”. In other words, the huge volume of cybersecurity warning signals (as many as 3,832 per day) was impacting their work.

We shouldn’t be surprised. Digital crime is exploding. Organisations currently experience an average of 1,938 cyber attacks per week.

This is putting immense pressure on SOCs. The quantity and complexity of threats – accelerated by AI-enabled attacks – has become too great. Cyber incidents no longer arrive with noise or warning. In terms of response, milliseconds matter.

It’s why many security leaders are exchanging a manual reactive approach to a proactive one. They’re employing three key strategies to do so:

•    Contextual threat intelligence 
SOCs collect information from social media, dark web and more to better understand the nature of threats.

•    AI tools for defence
Agents deploy generative and agentic AI to automate cyber defences.

•    Deeper collaboration
SOCs are evolving from isolated entities to globally connected networks – using orchestration platforms like SOAR (Security Orchestration, Automation and Response) to amplify AI-generated actions.

In this article, we will explore these factors and trace the emergence of the new proactive SOC. 

The evolution of the SOC – from static monitoring to autonomous entity

The first SOCs relied heavily on a human approach. Agents applied static rules and would manually adjust detection parameters. They would consult multiple data sources to correlate events and identify real threats.

However, escalating incidents exposed the system’s vulnerabilities. They resulted in false positives and negatives, analyst overload, limited detection, sluggish response times and more.

The next phase – SOC 2.0 – introduced more automation. SOCs introduced tools such as:

•    EDR (Endpoint Detection And Response). Monitors devices, such as computers and phones, to detect potential cyber threats.

•    SIEM (Security Information And Event Management). Software that collects and analyses security data from IT infrastructure to give an overall picture of security.

•    SOAR. Platforms that automate responses to incidents.

•    XDR (Extended Detection and Response). Lets security teams monitor all endpoint, network and cloud services from a single dashboard.

SOC 2.0 made improvements. It reduced the operational burden on analysts, but it still relied on human intervention for decision-making.

Which brings us to SOC 3.0 – the genuinely proactive security centre. SOC 3.0 leans on AI tools to:

•    Adjust detection rules as new attack patterns emerge
•    Correlate events from multiple sources in real-time
•    Automate deep-dive threat investigations
•    Assess the context of an incident and suggest the best course of action.

Know your enemy. The importance of ‘contextual threat intelligence’

For SOC leaders, it’s not enough to know merely what is happening. They must know more about the plans and motivations of attackers.

This is why SOCs now integrate data from sources such as social media, dark web, partner networks and specialist digital risk protection services (DRPS). Crunching this information into one portal can reveal answers such as:

•    Which group is behind a threat, their location, motives and methods
•    Most common threats by industry sector
•    Which threats are linked to a wider campaign or known malware type
•    Estimated speed of response and resolution
•    Which credentials have been exposed
•    Insight into criminal conversations about methods, incidents and targets

SOCs can use the insights to surface Indicators of Attack (IoAs) such as unusual login attempts, abnormal network traffic spikes or suspicious file modifications. They can also improve their awareness of actual incidents – Indicators of Compromise (IoCs) – based on IP addresses, domains, URLs and hashes.

The AI Inflection Point

The move from manual inputs to automation improved the SOC. But even sophisticated tools can’t solve the problem posed by smarter adversaries and rising alert volumes.

To do that, a bigger upgrade is needed. That upgrade is AI. Today, SOCs are deploying Gen AI and autonomous AI agents to execute single tasks and complex workflows. This is changing the approach to:

•    Threat detection: Machine learning algorithms identify patterns or anomalies and flag them as potential security breaches. They also speed up notifications to limit damage inflicted by attacks.

•    Reducing false positives. AI can more accurately distinguish between actual threats and harmless anomalies. This helps SOC teams focus on real threats.

•    Reporting: AI-driven tools deliver detailed, accurate reports (in natural language), which supports better decision making by human SOC agents.

Of course, AI can’t automate everything. Human expertise remains essential. Ultimately, analysts have to become ‘SOC pilots’, interpreting AI outputs, and choosing where and when they make decisions.

Industry reporting suggests AI SOC support is already delivering. A study by the Cloud Security Alliance found analysts aided by AI finished investigations up to 45 to 61 percent faster than  than teams with no assistance. 

Stronger together. Building cyber resilience through collaboration

Communication is a key component of cyber security. Silos within organisations – or between connected enterprises – will delay responses to incidents and leave gaps for attackers to exploit.

So there should be an internal culture of collaboration. Stakeholders  such as IT security professionals, risk analysts, senior execs, legal professionals, audit/compliance and human resources must work together to coordinate their defence against cyber threats.

As the previous section suggests, human and AI agents should team up too. Orchestration platforms like SOAR can help to amplify AI-generated actions.

But the need for collaboration goes wider. SOCs are evolving from isolated entities to globally connected networks. Leaders recognise the need for ‘global coverage, local presence’ ensuring they are supported wherever they operate. They’re working with security specialists, government and law enforcement to better understand the threat landscape and defence measures.

The next-gen SOC: Don’t react, anticipate.

The third iteration of the SOC is here. First, it evolved from a model of manual inputs to one deploying machine augmentation. Now, it’s moving to a new predictive model in which humans and AI agents work seamlessly together to create a super-powerful hybrid defender.

Here, machines no longer anticipate threats but continuously self-optimise to eliminate vulnerabilities before an attack. Agents filter out the noise, leaving human analysts to focus on what truly matters: mitigating risk, fast.