AI in Security Operations: Transforming SOCs or Overhyped?

Why AI in Security Operations Matters Today

For more than 15 years, cybersecurity has made use of artificial intelligence (AI) and machine learning, particularly in endpoint detection and response (EDR) tools. While early algorithms had trouble with specific attacks, contemporary AI can now analyse complex datasets, support decision-making in security operations, and do more than just detect threats.

AI adoption is now imperative as AI-driven threats emerge, including:

• Automated phishing campaigns (currently about 40% of phishing emails use AI)
• Adaptive malware
• AI-generated ransomware variants

Organizations need AI-driven solutions to enhance detection, automate response, and reduce false positives. However, AI alone cannot resolve underlying operational challenges - strategic implementation is essential.

The Power of AI in Security Operations

A strategic, engineering-led AI implementation can transform the SOC from reactive to proactive:

Enhanced Threat Detection

• Analyzes diverse datasets, including endpoint telemetry, network traffic, cloud logs, and threat intelligence
• Detects anomalies and advanced persistent threats (APTs) faster

Context-Driven Insights

• Enriches raw data with asset criticality, user identity, and historical patterns
• Helps analysts prioritize real threats over noise

Automated Response & Orchestration

• Triggers automated actions for routine alerts
• Lowers the average time it takes to fix things and cuts down on human error

Behavioral Analytics & Anomaly Detection

• Keeps an eye on how the system works all the time to look for small signs of compromise.
• Acts as an early warning system to stop things from getting worse.

Outcome: AI becomes a cyber co-pilot, making analysts more effective, less tired, and SOCs more efficient.

Why AI Is a Powerful Tool, Not a Silver Bullet

AI is not a panacea. Operational inefficiencies cannot be magically resolved by implementing AI in a SOC without addressing data quality or process problems.

• Broken processes remain broken: AI applied to untidy data may produce false positives or negatives.
• Overhyped tools: Like previous buzzwords like "zero trust," many vendors promote AI as a "solution."
• Patchwork SOCs: Systems become fragmented and siloed when AI is added without addressing underlying issues.

To get the most impact, CISOs should strategically integrate AI after concentrating on process and data quality improvement.

Best Practices for Leveraging AI in Security Operations

CISOs can optimise AI's effects by adhering to these guidelines:

1. Centralize Security Data: Combine logs, alerts, and threat intelligence so that AI has one place to get information.
2. Normalize & Enrich Data: AI works best with clean, contextual datasets that link events, assets, and users.
3. Integrate Automation Strategically: Use AI to do tasks that need to be done over and over again, but keep people in charge of making decisions that have a big effect.
4. Continuously Trained: AI models should always be trained and changed to reflect new threats and changes in the organisation.
5. Prioritize Business Impact: Allocating alerts and incidents according to risk to important assets rather than merely severity ratings.

The Future of AI in Cybersecurity

AI will continue to be a cornerstone of modern SOCs, enabling teams to:

• Recognise dangers before they become more serious.
• Automate routine research without sacrificing quality.
• Give practical advice to help guide strategic security choices.
• Bridge the gap between alert overload and operational efficiency.

However, the human element remains essential. Skilled analysts are needed to interpret AI outputs, make judgment calls, and respond to complex threats. AI’s true power lies in man + machine collaboration - where automation handles routine tasks and humans focus on strategic decision-making.

How Adaptive SOCs Harness AI

Current SOCs face challenges including expanding attack surfaces, growing complexity, and talent shortages. Adaptive SOCs, powered by AI, break free from rigid, centralized silos and focus on outcomes.

Netenrich enhances SOC operations by integrating:

• Google Cloud Security’s Gemini AI
• Mandiant threat intelligence
• Security Language Model (SecLM)

These AI-driven models are trained on extensive datasets to map threats, correlate patterns, and apply security policies in real-time, enabling:

• Real-time threat visibility
• Enhanced operational efficiency
• Proactive defense posture

Using machine learning and AI-driven anomaly detection, Netenrich Adaptive MDR Platform continuously adjusts to changing threats, expediting incident response and enabling security teams to respond quickly enough to fend off cyberattacks.

AI-enabled SOCs offer vital intelligence to safeguard operations, customer trust, and sensitive data, as 77% of organisations do not have an active incident response plan.

Together with Google Cloud Security, Netenrich provides a unified platform that unifies and contextualises all security data, bridging the gap between situational awareness and risk.

SOURCES:
VIPRE’s Email Threat Trends Report: Q2 2024