Where Are AI-Based Network Intrusions Being Detected at Unusual Timescales?

Table of Contents

• Beyond Human Perception: AI's New Sense of Time
• The Old Blind Spot vs. The New Vision: Human-Scale vs. Machine-Scale Detection
• Why This Is Happening Now: The 2025 Drivers of Extreme Timescales
• How AI Detects the Invisible: The Two Extremes
• Comparative Analysis: Where Different Timescale Attacks Are Detected
• The Core Challenge: Data Gravity and Model Drift
• The Future of Defense: Unifying Timescales with XDR
• CISO's Guide to Seeing the Full Timeline
• Conclusion
• FAQ

Beyond Human Perception: AI's New Sense of Time

In 2025, AI-based network intrusions are being detected at two opposite and equally unusual timescales where traditional security tools are blind: hyper-fast, "microsecond" attacks that are too brief for normal logging systems to capture, and hyper-slow, "months-long" campaigns that are too subtle for human analysts to notice. These extremes are most effectively being detected in high-frequency financial trading networks and critical infrastructure environments, respectively, forcing a complete re-evaluation of what "real-time" security truly means.

The Old Blind Spot vs. The New Vision: Human-Scale vs. Machine-Scale Detection

The traditional model of intrusion detection, built around signature-based tools and SIEMs, was implicitly designed for "human-scale" events. It focused on attacks that unfolded over minutes, hours, or days—a timescale that a human security analyst could reasonably investigate and respond to after receiving an alert. This created a significant blind spot for any activity that fell outside this perception window.

The new paradigm, driven by AI, is one of machine-scale detection. AI platforms can now perceive and analyze network events at two extremes. On one end, AI-powered Network Detection and Response (NDR) tools can spot malicious bursts of traffic that last mere milliseconds. On the other end, User and Entity Behavior Analytics (UEBA) platforms can identify a malicious campaign that has been slowly progressing for over a year. The AI sees the patterns that are both too fast and too slow for human cognition.

Why This Is Happening Now: The 2025 Drivers of Extreme Timescales

This shift to extreme timescale detection is driven by the evolution of both offensive and defensive capabilities.

Driver 1: The Automation of Attacks: Threat actors now use automated tools to launch high-frequency attacks, such as credential stuffing or DDoS bursts, that are designed to overwhelm defenses and achieve their goal in seconds.

Driver 2: The Sophistication of Nation-State APTs: Advanced Persistent Threat (APT) groups, often backed by nation-states, prioritize stealth above all else. Their "low and slow" campaigns are designed to fly under the radar for months or even years to achieve long-term espionage or sabotage goals.

Driver 3: The Data Processing Power of AI: It is impossible for humans to analyze the petabytes of data required to spot these extreme patterns. Only AI/ML models can process network traffic at the terabit-per-second scale needed to find micro-bursts or sift through years of logs to find a subtle behavioral anomaly.

How AI Detects the Invisible: The Two Extremes

AI uses fundamentally different approaches to detect intrusions at these two opposite ends of the spectrum.

To detect hyper-fast attacks, AI-powered NDR tools are placed directly on the network fabric, often analyzing traffic from packet brokers or network taps. They do not rely on slow, after-the-fact logs. Instead, they use machine learning models to analyze the statistical properties of traffic flows in real-time. The AI can instantly spot a sudden, anomalous spike in a specific type of traffic that lasts only a few milliseconds—a "micro-burst"—and flag it as a potential attack, something a SIEM would never even register.

To detect hyper-slow attacks, AI-powered UEBA and security analytics platforms ingest vast quantities of log data over very long periods—months or years. The AI builds a complex, multi-dimensional baseline of what constitutes "normal" behavior for every user and device. It can then detect a minuscule, but persistent, deviation from this long-term baseline. For example, it might flag that a specific database administrator's average data access volume has increased by 0.5% every week for the past eight months, indicating a slow, methodical data exfiltration campaign that would be completely invisible in daily or weekly reports.

Comparative Analysis: Where Different Timescale Attacks Are Detected

This table highlights the environments and technologies associated with different attack timescales.

The Core Challenge: Data Gravity and Model Drift

The primary challenge in operating at these extreme timescales is data gravity. Detecting micro-bursts requires processing potentially terabits of network data per second, which requires immense computational power. Detecting hyper-slow attacks requires storing and processing petabytes of log data for years, which has significant cost and complexity implications. Furthermore, for long-term detection, the AI faces the challenge of "model drift"—it must be intelligent enough to gradually adapt its baseline to legitimate changes in user behavior (like a promotion or a new project) without either flagging them as malicious or accidentally incorporating a slow attack into its definition of "normal."

The Future of Defense: Unifying Timescales with XDR

The future of advanced threat detection lies in platforms that can unify these different timescale detections. A sophisticated Extended Detection and Response (XDR) platform is the key. In the future, an XDR platform will be able to ingest a "micro-burst" alert from a real-time NDR tool and correlate it with a "slow-burn" behavioral anomaly from a UEBA platform that has been developing for months. By combining these signals, the XDR can reveal that what appeared to be two unrelated, low-priority events are actually two distinct phases of a single, highly sophisticated attack campaign, providing a level of insight that is impossible when these tools operate in silos.

CISO's Guide to Seeing the Full Timeline

To gain visibility across all timescales, CISOs must think strategically about their security investments.

1. Assess Your "Time-to-Detect" Gaps: Analyze your current security stack. Is it fundamentally blind to events that are shorter than a minute or longer than your log retention period (often just 90 days)? Understanding your visibility gaps is the first step.

2. Invest Based on Your Specific Threat Model: Your industry dictates your risk. If you are in high-frequency trading, real-time NDR is a critical investment. If you are a government agency or R&D firm guarding sensitive intellectual property, long-term log retention and advanced UEBA are paramount.

3. Demand Long-Term Baselines from AI Vendors: When evaluating AI security vendors, specifically ask about their "learning" or "baselining" period. A tool that only builds a 30-day baseline cannot detect a six-month attack campaign. Insist on solutions that support long-term behavioral analysis.

Conclusion

AI is fundamentally expanding the temporal boundaries of intrusion detection. By enabling security teams to see events at both hyper-fast and hyper-slow timescales, it is exposing the "human-scale" blind spot that has defined security operations for decades. This new vision, found today in the most demanding network environments, shows that true, comprehensive security requires the ability to detect threats that unfold not just in minutes and hours, but in microseconds and years.

FAQ

What is a "micro-burst" attack?

It is a malicious network event, like a DDoS burst or a rapid data transfer, that occurs in an extremely short timeframe (milliseconds to seconds), making it hard for traditional logging and monitoring tools to detect.

What is a "low and slow" attack?

It is a stealthy attack method, often used by Advanced Persistent Threats (APTs), where an attacker exfiltrates data or performs malicious actions in tiny increments over a very long period to blend in with normal network traffic.

What is NDR?

Network Detection and Response (NDR) is a category of security tools that continuously monitors network traffic to detect and respond to threats, often using AI to analyze behavior rather than just matching signatures.

What is UEBA?

User and Entity Behavior Analytics (UEBA) is a security technology that uses machine learning and statistical analysis to build a baseline of normal behavior for users and devices, and then detects deviations from that baseline.

What is an Advanced Persistent Threat (APT)?

An APT is a sophisticated, often nation-state-sponsored, threat actor who gains unauthorized access to a network and remains undetected for an extended period with the goal of long-term espionage or sabotage.

Why can't a SIEM detect these attacks?

A SIEM (Security Information and Event Management) system relies on collecting logs. Micro-burst attacks can be too fast to be logged properly, and low and slow attacks are too subtle to trigger standard SIEM correlation rules.

What is a packet broker or network tap?

These are hardware devices used in a network to create a copy of traffic, allowing security tools like NDR to analyze the traffic in real-time without impacting the performance of the live network.

What is "model drift" in AI security?

Model drift occurs when the AI's baseline model of "normal" behavior becomes outdated as legitimate user and system behaviors naturally change over time, potentially leading to more false positives or negatives.

How does XDR help with this problem?

Extended Detection and Response (XDR) helps by collecting and correlating data from multiple security layers (network, endpoint, cloud). It can link a fast NDR alert with a slow UEBA alert to reveal a single, more complex attack campaign.

Are these timescale threats relevant to all businesses?

While the most extreme examples are found in specific industries (like finance and government), the techniques are trickling down. Automated attacks are becoming faster everywhere, and all organizations are at risk of a persistent threat.

What is a "human-scale" blind spot?

It refers to the fact that traditional security was designed to detect events that are noticeable to human operators within a reasonable timeframe (minutes to hours), making it blind to events that are much faster or slower.

How is AI trained to find these patterns?

It is trained on vast datasets of real-world network traffic, including both normal activity and known attack patterns. This allows it to learn the statistical difference between benign and malicious behavior at different timescales.

Does this require storing all network traffic?

Not necessarily. Many NDR tools analyze traffic flows and metadata in real-time without storing the full packet content, which is more efficient. UEBA tools, however, do require long-term storage of log data.

Can an attacker deliberately evade long-term detection?

Yes, it's possible. A highly sophisticated attacker might try to slowly manipulate the AI's baseline over time. This is why human oversight and threat hunting are still critical components of security.

What is "data gravity"?

It's a metaphor describing the challenge of processing and analyzing massive datasets. As the volume of data grows, it becomes more complex and costly to move, store, and secure.

Is real-time detection the same as hyper-fast detection?

Not quite. "Real-time" often means within seconds or minutes. "Hyper-fast" or "microsecond" detection refers to identifying events that are faster than most standard "real-time" monitoring tools can process.

Where would I deploy an NDR tool?

You would typically deploy NDR sensors at critical network chokepoints, such as your internet egress, data center core, or to monitor traffic between different cloud environments.

Is UEBA focused only on users?

No. Modern UEBA platforms analyze the behavior of both human users (e.g., login times, data access patterns) and non-human entities (e.g., servers, applications, IoT devices) to create a complete behavioral picture.

What's the best first step to address these blind spots?

Conduct a risk assessment to understand your most likely threats. If your primary risk is fast-moving financial fraud, investigate NDR. If it's long-term IP theft, investigate UEBA and your log retention policies.

Will humans be completely removed from detection?

No. The role of humans will continue to shift to more strategic tasks: supervising the AI, hunting for novel threats that AI might miss, and making the final call on complex, high-stakes incidents.