The Missing Memory in Your Security Stack: How Attackers Exploit Stateless Systems

The Memory Gap in Threat Detection: Why “Statefulness” Wins

Security teams are facing a daunting challenge: today’s cyberattacks are slower, quieter, and more difficult to spot than ever before. Adversaries, from nation-state actors to malicious insiders, have mastered the art of flying under the radar. They stretch their activities over days, weeks, or even months, using legitimate credentials and tools to disguise their actions as normal business operations.

The uncomfortable truth is that most SIEM and XDR platforms are stateless. When it comes to threat detection, this is a critical flaw. This is why Exabeam developed New-Scale Analytics, a product built on a stateful foundation to address this very problem.

Think of it this way: a stateful system watches a story unfold, where each behavior adds to a character’s development before their actions trigger a conclusion. Stateless platforms, on the other hand, treat every event as a standalone snapshot. They only connect the dots if the events happen close together or match a rigid, predefined pattern. When an attacker’s activity is spread out, subtle, or intentionally disguised, the full picture is lost—and so is the opportunity to stop the attack.

Exabeam New-Scale Analytics, powered by our stateful Session Data Model, builds a persistent memory of user and entity behavior over extended periods. Every log, action, and anomaly is added to a living timeline that tells the complete story. This is how we help security teams see the threats that others miss.

The Problem with Stateless Detection

In computing, “stateless” means a system doesn’t remember anything from one interaction to the next. In threat detection, this means that once an event is processed and an alert is closed, it’s essentially forgotten. Most SIEM and XDR tools operate this way, which imposes three major limitations:

• Short Correlation Windows: They only connect activity within a brief, predefined time frame—often just minutes or hours. “Low-and-slow” attacks slide right through these cracks.
• Reliance on Predefined Rules: If an attacker’s methods don’t match an existing rule or playbook, the system won’t connect their behavior to earlier suspicious activity.
• Loss of Context After Closure: Once an incident is closed, the slate is wiped clean. If related behavior appears later, it’s treated as a new, unrelated event, forcing analysts to start their investigation from scratch.

This model might work for fast, noisy attacks, but it consistently fails against patient adversaries who know how to exploit these gaps.

How Stateful Threat Detection with New-Scale Analytics Works

A stateful system is behavioral—it remembers. In security, this means maintaining a long-term, evolving record of what a user, device, or account has been doing, and dynamically updating it with every new event.

Exabeam New-Scale Analytics is the engine that makes this possible. Its Session Data Model doesn’t treat each log as a standalone fact. Instead, it stitches them together into an ongoing session that can last for hours, days, weeks, or even months. This session includes the full sequence of actions associated with an entity, no matter how far apart they occur.

Because New-Scale Analytics tracks behavior across time and data sources, it builds a living narrative. Risk scores are updated as new activity is added, and a seemingly harmless action can take on new meaning in the context of the larger story. This enables Exabeam to spot an attack from the first hint of reconnaissance all the way to the final exfiltration, even if the attacker tries to hide in plain sight.

Case Study: A Stealthy Insider Threat

Here is a realistic scenario at a fictional financial services company, illustrating the difference between stateless tools and a stateful product like Exabeam New-Scale Analytics.

The Attacker: A disgruntled IT administrator with elevated privileges.
The Objective: Steal sensitive client data without tripping obvious alerts.
The Method: Use living-off-the-land (LotL) techniques, relying on legitimate tools already in the network.

Timeline	Attacker’s Action	Stateless XDR / SIEM Response	Exabeam (Stateful) Response
Day 1	Logs in after hours via VPN from home.	Flags as a low-priority anomaly. Analyst dismisses it as late-night work.	Adds to the user’s ongoing session timeline, noting a deviation from normal behavior patterns.
Day 3	Runs PowerShell to enumerate Active Directory groups.	Sees it as an isolated PowerShell execution. No connection to previous activity.	Links the PowerShell command to the Day 1 login in the same timeline, recognizing it as potential reconnaissance. The user’s risk score increases.
Day 7	Connects via RDP to a rarely used file server.	Generates a standalone RDP alert but doesn’t link it to the other suspicious events.	Merges the RDP connection into the same behavioral chain, further escalating the user’s risk score. The narrative of the attack becomes clearer.
Day 12	Uses Robocopy to stage large volumes of client data.	Treats it as a one-off unusual file copy activity. Another isolated alert is generated.	Adds this action to the growing session. The massive data staging significantly raises the risk score, highlighting a probable breach in progress.
Day 14	Uploads data to personal cloud storage over HTTPS.	No alert. The activity is lost in normal encrypted web traffic.	Recognizes the upload to an atypical domain as the conclusion of a two-week attack campaign. An incident is automatically created, triggering an immediate, high-priority response.

Where Most XDR Platforms Fall Short

XDR is often marketed as being context-aware, but most platforms today are still only semi-stateless. They correlate alerts in short bursts but fail to hold onto the story over time. This creates major blind spots that Exabeam New-Scale Analytics is designed to eliminate. Without a truly stateful engine, other tools suffer from:

• Inability to track low-and-slow threats due to short correlation windows.
• Limited to predefined patterns, missing novel or evolving attack techniques.
• Lost context after incidents are closed, leading to repeated work.
• Weak entity tracking across different devices, IP addresses, or user aliases.

The Payoff of a Stateful Approach

Moving from a stateless to a stateful detection model with Exabeam New-Scale Analytics delivers powerful, measurable benefits that strengthen your entire security posture.

• Dramatically Fewer False Positives: By understanding behavior in context, you can distinguish real threats from benign anomalies.
• Faster, More Efficient Investigations: The attack timeline is pre-built, saving analysts from having to manually piece together evidence.
• Higher Detection Rates for Advanced Threats: Uncover the stealthy, multi-stage attacks that other tools miss.
• Reduced Dwell Times and Breach Impact: Stop attacks earlier in the kill chain, minimizing damage.
• Maximize Your Existing Investments: New-Scale Analytics enriches and leverages data from your existing security stack—regardless of the vendor.

It’s Time to Think in Sequences, Not Snapshots

Attackers think in campaigns, not isolated events. If your detection platform can’t remember what happened yesterday, last week, or last month, you’re giving adversaries a critical head start.

Exabeam New-Scale Analytics changes the game. By maintaining a continuous, contextual record of all user and entity behavior, it empowers you to see the entire attack story, not just disconnected chapters.

Ready to see the difference for yourself? Schedule a demo today and see New-Scale Analytics in action. Compare how your current system views an incident versus how Exabeam builds the complete story. It could be the key to stopping your next breach.