Cybersecurity’s Early Warning System: How Live Network Traffic Analysis Detects The ‘Shock Wave’ Before the Breach ‘Tsunami’
According to IBM, it takes businesses about 197 days on average to identify a data breach and another 69 days to contain it. That response time in the context of today’s threat landscape is simply unsustainable, especially when you consider that organizations are spending more than ever on cybersecurity tools and yet are still missing signs of an attack.
Security teams can analyze live network traffic, an approach also known as network detection and response, and be more proactive in detecting the warning signs of an impending breach. It’s the difference between acting on a shock wave before a tsunami and scrambling in the aftermath of a giant wave when it’s too late to stop the damage.
The sense of urgency is even greater for lean security teams, mid-market enterprises and MSSPs, who are constantly challenged to detect and mitigate threats on smaller budgets with fewer resources before they escalate into full-blown security incidents.
Why Network Traffic is Often the First Indicator of an Attack
Unlike endpoint detection or signature-based security tools, network traffic is always in motion, and therefore is always revealing patterns. It provides security analysts with an unfiltered view of interactions between users, applications, and systems, whether on-premises, in the cloud, or across hybrid environments. The ability to observe, analyze and act on live network traffic flows is crucial for identifying threats before they cause damage.
Too many security tools are designed to focus on collecting data from security event logs, better known as SIEMs. These are now a staple in most enterprise security tech systems. The problem is, legacy SIEMs collecting data from logs simply aren’t enough in today’s complex landscape. Why? Security logs are a moment in your organization’s past; a snapshot of your network’s historical data. By the time your security team receives an alert from your traditional SIEM and spends hours investigating, it may already be too late. To succeed, this must be part of a holistic defense strategy that brings together data from logs and real-time network traffic analysis. Security teams need to receive accurate alerts at near-real-time speed so they don’t waste time on false positives while real threats slip through the cracks.
Security teams seeking to build more resilient systems should consider adding live network traffic monitoring capabilities to their arsenal of tools. Live network traffic monitoring assists with three important early indicators of possible compromise:
- Repeated login attempts: When these attempts are from unusual locations or at odd hours, this is often a signal of credential stuffing or brute-force attacks. Attackers will cycle through thousands of username-password combinations until they can gain access.
- Attempted or successful lateral movements: Attackers who successfully gain access to an account immediately attempt to move laterally through a compromised network. They often leverage stolen or compromised credentials to access critical systems, establish persistence and evade detection. Live network traffic analysis will reveal this as unusual user behavior.
- A user suddenly accessing unfamiliar systems: Live network traffic analysis will ring the alarm when it picks up patterns of Anomalous Traffic Flows and Behavioral Changes. For example, when a key employee who normally accesses certain business applications suddenly generates unexpected traffic patterns, this is a major red flag that a hacker may have taken control.
A quick word about security logs: Typically, before an account takeover succeeds, network logs will show a dramatic uptick in failed authentication attempts, signaling the attack wave in motion. Security teams analyzing network authentication logs will spot these irregular access attempts before an attacker can gain full control. However, analysts need assistance from advanced technology and automation to accelerate this process. AI-based behavioral analytics applied to network traffic can quickly identify deviations from normal behavior, providing early warning signals. Today’s advanced AI can deliver greater context around these anomalies, so these behaviors can be flagged and escalated to the analyst, prioritizing them over other low-level alerts.
Live Traffic Flows: The Shock Wave That Precedes Disaster
A cyberattack rarely begins with a loud bang. The last thing an attacker wants to do is draw attention. Instead, it starts with subtle tremors; network signals indicating that a deeper problem is brewing. Security teams must recognize these patterns early to prevent full-scale breaches.
Consider a real-world scenario:
- An organization detects a spike in failed login attempts from an offshore IP address.
- Shortly after, a successful login occurs from that same IP, but the user typically works from a different country.
- The account begins accessing sensitive files at 2 AM, a behavioral anomaly compared to their normal working hours.
- Minutes later, large outbound data transfers appear in the traffic logs, suggesting an active exfiltration attempt.
At this point, a traditional security tool relying only on endpoint alerts or SIEM might miss the attack until it’s too late. However, by monitoring network traffic in real time, an MSSP or mid-market security operations team could have the power to stop this attack before data is stolen.
Network Visibility: The Foundation of a Human-Augmented Autonomous SOC
Leveraging live network traffic analysis is a crucial step in building an Autonomous SOC in which humans are still at the heart of daily operations. By integrating AI-powered traffic analysis with automated response mechanisms, organizations can identify threats earlier, before an attacker gains control. They can also reduce manual investigations by correlating network traffic anomalies with other security signals. Automation means they can scale security without the cost of scaling teams.
The true power of network traffic analysis is being able to see the storm forming before it hits. By focusing on network activity as the first indicator of an attack, security teams buy themselves critical time, turning a potential disaster into a manageable incident. The next time a security event occurs, make sure your team is catching the shock waves early.
