AI has transformed network monitoring. Here’s how to adapt
Network security monitoring is getting more complicated, and it’s not just because attackers are getting craftier. AI is showing up in two places that matter to your security setup: in the SIEM (security information and event management) and monitoring tools you use to catch threats, and in the business applications that create new attack surfaces you need to watch.
The security tool vendors are all shouting about AI features, but separating genuine improvements from rebranded analytics with “AI” slapped on the marketing materials isn’t always easy. Meanwhile, those same AI-enhanced business apps your users love are generating traffic patterns that can hide real threats or trigger false alarms.
According to the Spiceworks 2025 State of IT Report, 52% of organizations currently use SIEM platforms, with another 16% planning to deploy them within two years. Companies are investing heavily in AI—IDC research shows large companies will spend over 40% of their core IT budgets on AI by 2025. This has SIEM vendors scrambling to add AI features to stay competitive. The result: more AI-powered security tools flooding the market while AI applications create new network traffic patterns that can hide real threats.
Distinguishing real AI from marketing hype in SIEM tools
AI integration in SIEM platforms can actually improve your threat detection, especially if you’re running a lean security operation. Traditional SIEM tools often drown you in alerts that turn out to be nothing, but SIEM solutions with properly implemented AI features are getting better at distinguishing between actual threats and normal network weirdness.
AI isn’t just changing how cybercriminals launch their attacks—it’s also changing what normal network behavior looks like and how security monitoring works.
Tools like Splunk’s AI-powered threat detection and IBM QRadar’s Watson integration now use machine learning to establish behavioral baselines for your network. Instead of solely relying on signature-based detection that misses novel attacks, these systems learn what normal looks like in your environment and flag genuinely suspicious activity.
But here’s the thing: not every “AI-powered” security tool actually uses AI in meaningful ways. Real AI enhancement means the tool learns and adapts to your environment over time, correlates events across multiple data sources, and reduces false positives through contextual analysis. Fake AI enhancement is just traditional rule-based detection with machine learning buzzwords in the marketing copy.
Look for tools that can explain their reasoning when they flag something suspicious. If a SIEM platform alerts you to potential lateral movement, genuine AI should be able to tell you why it connected those specific events and what made the pattern unusual compared to normal network behavior.
New security challenges from AI business applications
The AI features showing up in your small business applications create security monitoring challenges you probably haven’t dealt with before. Office 365 Copilot, Salesforce Einstein, and similar AI-powered tools generate network traffic that can hide malicious activity or trigger security alerts you’re not sure how to interpret. You’ll want to keep a lookout for:
Unpredictable traffic patterns
Sometimes, AI-related traffic doesn’t follow the predictable patterns that traditional security monitoring expects. When users upload sensitive documents to AI services for analysis, that creates data exfiltration patterns that might look suspicious to your data loss prevention (DLP) tools.
Hybrid work complications
If your organization allows hybrid work, your situation is more complex. Remote employees using AI tools create network traffic that flows through home internet connections and VPNs in ways that can obscure attack indicators. If someone’s compromised home network is being used for command and control traffic, that malicious activity might get lost in the noise of legitimate AI application traffic.
Third-party integration blind spots
Many business applications now send data to external AI services through APIs, creating new potential data leak paths that bypass your traditional monitoring. A compromised customer service platform might exfiltrate chat logs through its AI sentiment analysis integration, for example, or attackers could abuse AI-powered expense categorization features to hide fraudulent transactions in legitimate-looking data flows.
Practical security monitoring strategies for AI-era networks
Your SIEM needs to understand all of these traffic patterns to distinguish between legitimate AI usage and actual threats. Use a methodical approach to prepare your security monitoring that includes both the AI tools you’re already using and the new ones you’re planning to deploy.
1. Audit current AI usage
Audit your current AI usage from a security perspective. Check what data your Office 365 Copilot instance can access, review which external AI services your business applications connect to, and identify any shadow AI usage in which employees might be using unauthorized tools with company data.
2. Establish AI baselines
Start by documenting normal AI application behavior before expanding your AI usage. Create baselines that include not just bandwidth and timing patterns, but also the types of data being uploaded, which users access which AI services, and what external endpoints your AI tools connect to. This baseline becomes crucial when investigating potential security incidents.
3. Update SIEM correlation rules
Update your SIEM correlation rules to account for AI traffic patterns. If your sales team regularly uploads prospect lists to AI-powered lead scoring tools, make sure your data loss prevention rules understand that this is normal behavior. But also ensure you’re monitoring for unusual patterns—like someone uploading your entire customer database outside normal business hours. Test whether your SIEM’s “AI-powered” features actually reduce false positives and improve threat detection, or if they’re just traditional analytics with new labels.
4. Configure threat detection
Configure your network detection and response tools to distinguish between legitimate AI traffic and potential threats. Set up monitoring that tracks data flows to AI services, flags unusual upload volumes or timing, and correlates AI usage with user behavior analytics. Before deploying new AI tools, assess their security monitoring requirements and potential blind spots.
5. Monitor sensitive workflows
Focus your security monitoring on AI-dependent workflows that handle sensitive data. According to CIO.com reporting on IDC research, 67% of AI spending in 2025 will target embedding AI into essential business functions. If your HR team uses AI for resume screening or your finance team relies on AI for fraud detection, these systems need dedicated security monitoring because they’re handling your company’s most sensitive information.
The bottom line
AI isn’t just changing how cybercriminals launch their attacks—it’s also changing what normal network behavior looks like and how security monitoring works. Getting ahead of both trends will help you support the network traffic demands associated with AI-related applications while avoiding alert fatigue from AI-generated false positives. Ultimately, you’ll be in a far better position to catch and neutralize real threats when they arrive.
