The Reveal Platform
Modern Security Operations Centers (SOCs) are currently facing a paradox: they have more data than ever before, yet they have never been more blind. Analysts are drowning in thousands of daily alerts, most of which lack the context needed to tell a coherent story. While security teams are busy silencing the noise, attackers have evolved to operate in the shadows between these signals.
The most dangerous of these modern threats is the Ghost SPN attack — a stealthy identity maneuver designed to bypass traditional defenses entirely. Here are the top takeaways on why this attack is so effective and how AI is changing the way we fight back.
Modern SOC teams are overwhelmed with alerts — thousands per day — most of which lack context, correlation, or clear investigative direction. Traditional SIEM platforms generate alerts based on isolated events:
Individually, these events appear benign. But attackers don’t operate in isolated events — they operate across identity, endpoint, network, and time.
Gurucul NG-SIEM, powered by UEBA and AI, transforms fragmented alerts into a unified, high-confidence incident by correlating multi-source telemetry (AD, Kerberos, authentication, network), applying UEBA-based behavior analytics, linking events across time into a single narrative, and delivering analyst-ready insights with context and recommended actions.
This is not just alert aggregation — it is attack reconstruction.
One of the most advanced identity-based attack techniques today is the Ghost SPN attack. In this technique, an attacker temporarily assigns a Service Principal Name (SPN) to a user account, requests Kerberos service tickets for credential extraction, then rapidly removes the SPN to erase forensic evidence, and finally leverages the compromised credentials for lateral movement and privilege escalation. This attack is specifically designed to evade traditional detection.
Traditional Security Information and Event Management (SIEM) platforms are built to flag isolated events, which is exactly what attackers count on. To a standard SIEM, the individual steps of a Ghost SPN attack look like routine administrative work:
Because these events are treated in isolation, they never trigger a high-priority alarm. It is a sobering reminder that a “green” dashboard doesn’t necessarily mean you are safe; it might just mean your tools aren’t looking at the right narrative.
Traditional SIEMs treat each of these events in isolation, generating no coordinated alert or only low-priority noise:
| Event | Traditional SIEM Interpretation |
| SPN Added (Event 5136) | Admin activity |
| Kerberos TGS Request (Event 4769) | Normal Kerberos behavior |
| SPN Removed (Event 5136) | Cleanup / config change |
| Login (Event 4624) | Successful authentication |
Result: No alert, or multiple low-priority alerts with no attack context.
Without Gurucul, these are the individual alerts a traditional SIEM would surface — each appearing as a standalone, low-context signal with no narrative connecting them to an active attack:
Gurucul correlates these events into a single AI-driven incident, reconstructing the full attack chain: a temporary SPN assignment, followed by Kerberos ticket activity and rapid cleanup — indicative of stealth credential extraction — and then by suspicious login and privilege escalation.
The Gurucul AI Incident Management screen provides a complete, analyst-ready view of the attack. Here is the incident overview for INC-4132: Stealth Kerberoasting Attack via SPN Abuse, classified Critical.
The AI-driven incident summary provides three critical panels simultaneously: what happened, key indicators, and the next steps the analyst should take. Each section is auto-generated from correlated telemetry — no manual triaging required.
Gurucul’s AI reconstructs the full attack chain as a readable narrative: SPN assigned to a non-service user account → Kerberos service ticket requested with weak RC4 encryption → SPN removed shortly after (anti-forensics) → suspicious external IP login → privilege escalation to Domain Admins. The entire sequence occurred within an 11-minute window, with the SPN lifecycle completing in under 4 minutes.
| Time | Activity |
| 09:01 | SPN Added to user account (Event 5136) |
| 09:02 | Kerberos TGS Ticket Requested with RC4 encryption (Event 4769) |
| 09:04 | SPN Removed — anti-forensic cleanup (Event 5136) |
| 09:10 | Suspicious login from external IP (Event 4624) |
| 09:12 | Privilege Escalation to Domain Admins (Events 4732 / 4672) |
As shown in the screenshots below, the incident management workflow enables AI-assisted investigations without requiring manual prompting. This allows SOC analysts to gain a deeper understanding of each incident, supported by an accurate execution plan that includes detailed steps, an executive summary, and recommended playbooks.
| Capability | Traditional SIEM | Gurucul NG-SIEM + UEBA |
| Detection approach | Alert-centric, event-by-event | Identity-centric, behavior + context |
| Context | None — isolated events only | Full cross-domain correlation |
| Alert volume | High false positives, analyst fatigue | 90%+ alert noise reduction via compression |
| Investigation | Manual, time-consuming | AI-driven, analyst-ready with guided next steps |
| MTTD | Days to weeks (if detected at all) | Minutes — automated triage and prioritization |
Organizations using Gurucul AI SOC capabilities typically see:
Each AI-generated incident includes root cause analysis, risk scoring and prioritization, and clear analyst action steps. For the Ghost SPN incident, Gurucul’s Sme AI recommends:
A final thought to ponder: If a “ghost” entered your network today and stayed for only 11 minutes, would your security system tell you a story, or would it just give you more noise?
Stealth identity attacks, such as Ghost SPN, are specifically designed to bypass traditional detection systems. They exploit the gaps between isolated event logging, leaving no single signal strong enough to trigger a conventional SIEM alert. But when you move from alert monitoring to attack understanding, from event correlation to identity intelligence, you don’t just detect threats. You understand and stop them faster.
Gurucul NG-SIEM detects attacks at the entity level, not just the event level, by combining UEBA behavior baselines, risk scoring, cross-domain telemetry correlation, and AI-driven reasoning. The result is a platform that moves security teams from reactive alert monitoring to proactive understanding of attacks. Ready to Transform Your SOC?
Contributors:
Naveen Vijay
Karan Chawla
A Ghost SPN attack is a stealthy identity-based technique where an attacker temporarily assigns a Service Principal Name (SPN) to a standard user account to request Kerberos service tickets. This allows them to perform offline credential cracking (Kerberoasting). To evade detection, the attacker rapidly removes the SPN to erase forensic evidence before using the stolen credentials for lateral movement.
Traditional SIEMs struggle because they analyze security events in isolation rather than as a connected story.
Speed is the attacker’s greatest defense.
Detecting these attacks requires looking for specific, correlated anomalies:
AI-powered platforms like Gurucul NG-SIEM use User and Entity Behavior Analytics (UEBA) to compress thousands of fragmented alerts into a single, high-confidence incident.
