Security teams have never had more tools, more data, or more pressure. Every advisory claims urgency, every new exploit seems automated, and every threat actor is now experimenting with AI. Yet most breaches still succeed not because defenders lack tools—but because they lack complete visibility and the automation to make sense of what they see.
To understand what’s happening across your environment, you need three complementary signal streams: logs, endpoint telemetry, and network traffic. Each exposes a different dimension of an attack. Each catches what the others can’t. And when you combine them with modern AI—machine learning, agentic triage, and LLM-powered copilots—you finally get a security program that can keep up with attackers.
Logs: The Record of Intent
Logs tell the story of “what was reported”—authentications, API calls, privilege changes, configuration drifts. They reveal intent.
Example: Privilege Escalation
A compromised user logs in and immediately attempts admin-level changes. Logs show:
- Suspicious login geography
- IAM modifications
- Unusual API activity
- Token creation for lateral access
Endpoint Telemetry: The Truth of Execution
Endpoints reveal what code actually ran: processes, binaries, scripts, memory activity, persistence mechanisms.
Example: Hidden Malware
An attacker drops a fileless payload. Endpoint telemetry shows:
- PowerShell spawning unexpectedly
- Living-off-the-land tools abused
- Registry persistence
- Local privilege escalation attempts
Network Traffic: The Undeniable Signal
Network traffic is physics—you can’t fake packet flow. It shows what happens between systems, including those you can’t install agents on (OT, IoT, legacy).
Example: Data Exfiltration
A compromised server begins sending encrypted chunks externally. Network analytics reveal:
- Outbound spikes
- New C2 tunnels
- Exfiltration outside business hours
- Lateral connections preceding the attack
ML models catch unusual patterns in volume, directionality, and timing—surfacing exfiltration attempts early.
How AI Changes the Game
Seeing logs + endpoints + network is essential.
Making sense of all three in real time is impossible for humans alone.
Today’s SOCs rely on three layers of AI:
1. Machine Learning for Detection
2. Agentic AI for Triage
- Collecting evidence from all telemetry
- Reconstructing attack sequences
- Mapping entities and assets involved
- Determining likely root cause
- Ranking real risk
Across Stellar Cyber deployments, agentic triage consistently delivers:
- up to 90% reduction in alert volume
- 80–90% auto-triage of routine cases
- 70%+ improvement in MTTR
3. Copilot (LLM) Assistance
The Best Core: SIEM + Network (With Open Endpoint Choice)
SIEM (logs) + Network Traffic (NDR)
- Logs provide identity, governance, and intent.
- Network traffic reveals lateral movement and exfiltration.
- Both are always available—even where endpoint agents cannot go.
- Endpoint tools change; open architectures mean you can use any EDR you prefer.
Stellar Cyber unifies all three signals into one AI-powered platform, enabling machine learning, agentic AI, and copilot capabilities across the entire environment.
Because visibility + automation isn’t optional anymore. It’s the only way to stay ahead of adversaries who are already using AI against you.
