All alerts are not equal.‍

Yet somehow, every alert becomes the SOC’s problem.

Every day, SIEM and CNAPP tools flood the SOC with alerts — but take a closer look, and they generally fall into four categories

1. Active Threats (<1%)
2. Hygiene Issues (e.g., misconfigurations, privilege misuse)
3. Low-Level Signals or Events of Interest (e.g., failed logins, phishing attempts)
4. Legitimate Activity / False Positives (e.g., admin activity, VPN geo anomalies)

Most SOC teams are laser-focused on Category 1: actual threats.

👉 But what about Categories 2, 3, and 4?

They’re often dismissed as noise. But should they be?

🔍 Breaking It Down

2. Hygiene Issues

These aren’t immediate threats, but they represent persistent exposure — and often go unresolved.

Examples:

• Unrotated credentials
• Open high-risk ports
• Privilege misuse by internal users

These stem from systemic issues: policy gaps, poor enforcement, or unclear ownership. While the SOC may not own these risks, someone must — whether it’s DevOps, GRC, or Security Engineering.

3. Low-Level Signals or Events of Interest

Things like:

• Phishing emails
• Brute-force login attempts
• Port scans

Individually? Low risk.

But in aggregate — especially when mapped to MITRE ATT&CK — they can reveal attacker behavior.

What’s needed: 

✅ Behavioral analytics
✅ MITRE-based correlation

4. Legitimate Activity /False Positives

These are tricky. Often, they’re normal behavior misunderstood by rigid detection rules:

• VPN IP changes triggering landspeed alerts
• Admin actions misclassified as anomalous behavior

Why? Because alerts lack full business context. And humans often rely on tribal knowledge or outdated documentation.

What helps: 

✅ Whitelisting & tuning
✅ Risk acceptance policies
✅ AI that can learn organizational context

🚨 The Problem With Today’s “AI SOC” Approach

Can we just throw all these alerts at an “AI SOC automation” platform and expect magic?

Not quite.

That’s like handing a broken triage model to a Tier-1analyst or MSSP and expecting a better outcome.

Task automation ≠ Outcome improvement.

🔧 What’s Needed Before AI-Based SOC Automation

Before you automate, you need:

✅ Clear alert categorization
✅ Root cause analysis across alerts
✅ MITRE-based behavior almodeling
✅ Contextual understanding of business risk

🧠 AiStrike: AI SOC That Understands Before Acting

At AiStrike, we take a Composite AI approach— combining:

• Machine Learning for pattern recognition
• MITRE-based Knowledge Graphs for threat context
• Generative AI for reasoning and investigation

Our platform doesn’t just automate triage — it thinks before it acts.

✅ 1. Machine Learning for Pattern Recognition

We classify and group recurring alerts, tying them to systemic root causes like policy gaps or configuration drift — and route them to the right teams.

We also baseline normal behavior across identities,endpoints, networks, and cloud environments — to detect meaningful deviations.

✅ 2. Knowledge Graphs for Threat Context

We map low-level events to MITRE ATT&CK, exposing stealthy tactics like:

• Privilege escalation
• Lateral movement
• Credential abuse

Even if signals are disjointed across EDR, SIEM, IAM, and cloud tools, our knowledge graph links them into a meaningful narrative.

✅ 3. Generative AI for Noise Suppression & Investigation

Once alerts are categorized and correlated, our Gen AI agent:

• Ranks low-level signals based on business context
• Assesses impact (data exfiltration, privilege escalation, etc.)
• Investigates and documents every step
• Provides remediation guidance with context

It doesn’t just classify — it explains.

‍✅ Human-in-the-Loop Automation

Our automation engine supports flexible response workflows— from fully automated to analyst-approved.

And it keeps learning:

• From tribal knowledge
• From past investigations
• From your environment’s unique business logic

So even “legitimate but unusual” activity — like emergency admin access — is classified correctly, without unnecessary noise.

🧩 Final Thought

If you’re evaluating an AI SOC solution, don’t get distracted by buzzwords like agentic AI.

Instead, ask:

• How does it categorize and contextualize alerts?
• Can it correlate behavior using MITRE ATT&CK?
• Does it support intelligent, automated, and explainable response?

At AiStrike, we’re not just building faster automation —we’re building smarter decision-making for modern security teams.