From Searching Security Data to Investigating Threats: The Three Layers of Rover

Modern organizations are generating an enormous amount of security telemetry—identity events, endpoint logs, cloud activity, network data, application logs and audit trails. The challenge is no longer simply storing this data. The real challenge is being able to quickly answer:

This is where Rover takes a different approach. Rather than treating a SIEM as only a search and alerting system, Rover can be understood as a platform built around three connected layers: Smart Search, Security Context, and Agentic Investigation.

                 ROVER
                    │
        ┌───────────┼───────────┐
        │           │           │
        ▼           ▼           ▼
   SMART SEARCH  SECURITY     AGENTIC
                CONTEXT      INVESTIGATION
        │           │           │
        ▼           ▼           ▼
   Find relevant  Understand  Build attack
   evidence       entities    narrative
        │           │           │
        └───────────┼───────────┘
                    ▼
             EVIDENCE-CITED
                  CASE
                    │
                    ▼
                RESPONSE

1. Smart Search — Find the relevant evidence

The first layer is about speed and efficiency.

Security data can remain in low-cost object storage such as S3 instead of forcing organizations to move everything into expensive hot storage. The challenge then becomes: how do we find the right information quickly across massive historical datasets?

Rover's search layer is designed to make that historical data searchable and help identify the relevant evidence without requiring analysts to manually search through enormous volumes of telemetry.

The goal isn't simply:

“Search my logs.”

It is:

“Find the small amount of data that matters to this investigation.”

This becomes increasingly important as organizations retain months or years of security telemetry.

2. Security Context — Understand what the data means

Finding an event is only the beginning. A single event rarely tells the complete story.

Consider:

User
  │
  ├── Device
  │
  ├── IP Address
  │
  ├── Application
  │
  ├── Cloud Account
  │
  └── Resources

Security context connects these entities and their activities across different sources. Instead of seeing:

“User X logged in from IP Y.”

the investigation can ask:

• Is this normal for the user?
• Which device was involved?
• What happened immediately before the login?
• What cloud resources were accessed afterward?
• Has this IP appeared elsewhere?
• Did the user's behavior change?
• What other identities or systems are connected to the activity?

This transforms isolated log records into security context. The result is a much richer understanding of who, what, where and when.

3. Agentic Investigation — Build the story

This is where Rover can move beyond traditional SIEM workflows. Instead of requiring an analyst to manually execute dozens of searches and pivots, an AI agent can drive the investigation.

A simplified workflow looks like:

Signal
  ↓
Investigation hypothesis
  ↓
Search
  ↓
Entity correlation
  ↓
Historical investigation
  ↓
Evidence collection
  ↓
Timeline construction
  ↓
Hypothesis validation
  ↓
Case

The agent doesn't simply produce an AI-generated summary. The important principle is evidence-backed investigation. The agent should be able to explain:

• What happened
• When it happened
• Where it started
• Which entities were involved
• Which events support the conclusion
• How the activity progressed
• What remains uncertain
• What should be investigated next

This turns AI from a chat interface over a SIEM into an investigation engine.

From Events to Evidence-Cited Cases

The three layers work together:

SMART SEARCH
     │
     │ finds relevant evidence
     ▼
SECURITY CONTEXT
     │
     │ connects entities and activity
     ▼
AGENTIC INVESTIGATION
     │
     │ reasons over the evidence
     ▼
EVIDENCE-CITED CASE
     │
     │
     ▼
RESPONSE

The key difference is that Rover isn't just trying to return more search results. It is trying to reduce the distance between:

The Shift in SIEM

Traditional SIEM workflows often look like:

Alert ➔ Analyst searches ➔ Search again
      ➔ Pivot to another source ➔ Search again
      ➔ Correlate manually ➔ Build timeline
      ➔ Write investigation summary

Rover's vision is:

Signal
  ↓
Rover
  ├── Search
  ├── Correlate
  ├── Investigate
  ├── Gather evidence
  └── Build timeline
        ↓
   Evidence-Cited Case
        ↓
      Response

The Rover Philosophy

The three layers can ultimately be summarized in three questions:

Smart Search: Where is the evidence?

Security Context: What does the evidence mean?

Agentic Investigation: What happened, why does it matter, and what should we do next?

And that leads to the central Rover proposition: