Early Threat Detection: Using AI as the First Line of Defense

The longer a threat remains undetected, the more costly and disruptive it becomes. This is particularly concerning given that, on average, attackers stay hidden within a network for 24 days, as highlighted in Verizon’s 2025 Data Breach Investigations Report (DBIR). 

The data breaches resulting from those attacks hit the organizations quite heavily. According to IBM’s Cost of a Data Breach Report 2025, the average cost of a data breach is $4.44 million. The bright side is that this figure is 9% lower than the previous year, thanks to faster threat detection and response (TDR). 

If fast threat detection and response can significantly reduce the global average cost of a data breach, imagine what early detection can do. In this post, we explore early threat detection — what it actually means, why it matters, and the role AI plays in it.

What Is Early Threat Detection?

Early threat detection is a proactive cybersecurity approach that’s focused on identifying potential cyber threats and malicious activities at the earliest possible stages, ideally before they can infiltrate a network, proceed to lateral movement, or execute malicious commands. 

The security strategy has two components:

• Preventive
• Reactive 

This means that there’s both an element designed to block known cyber threats and vulnerabilities from the outset (preventive), as well as a part that monitors network activities continuously for anomalies and implements automated playbooks to immediately respond to identified cyber threats (reactive).

Early threat detection makes TDR much more effective. If cyber threats are not detected at the initial stages, response efforts become more costly and likely to involve more operational disruptions. For example, detecting malware immediately after an employee accidentally clicks on a phishing link means that the security team (or the endpoint detection and response software) would only need to contain and wipe it from the affected machine as a precaution. No major processes need to be stopped to ensure containment.

Without early detection, the malware would enable the attacker to gain a foothold, allowing lateral movement throughout the enterprise network. As you can imagine, even the most advanced security program won’t be able to match attackers who are able to operate with long dwell times (the amount of time the threat stays undetected). By the time the threat is finally detected, it may have already achieved whatever the threat actor wanted to achieve — performed privilege escalation, stolen sensitive data, or deployed ransomware.

Why Early Threat Detection and Response Matter

If we sum up what we’ve discussed above, it’s clear that the impact of early threat detection goes beyond threat prevention and extends to the following:

• Damage control: Shorter dwell time results in less damage. Attackers won’t have enough time to move across the network, escalate privileges, steal data, or execute more damaging attacks. Earlier, preventive blocking means no damage at all, which is even better. 
• Regulatory compliance: Industry regulations and data privacy laws require strong security controls and detailed incident response plans, which include timely breach notification. The U.S. government, for instance, has made cyber incident reporting mandatory for critical infrastructure. Security standards such as the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR) require covered entities to notify relevant parties about any incident in a timely manner (as early as within 72 hours of detection for GDPR).
• Financial savings: The cost of remediating a breach increases in proportion to the threat’s dwell time. For example, phishing could pave the way for ransomware, if not prevented early, and this threat cost organizations between $300 to $3.6 million in ransomware payments in 2024. Early detection helps minimize these costs by preventing large-scale data loss, system downtime, and reducing extensive incident response efforts.
• Reputational protection: A public data breach can severely damage an organization's reputation. It erodes customer trust and diminishes brand loyalty. Research shows that 75% of U.S. consumers would avoid buying from a company that suffered a cyber incident. A security program that includes early detection helps protect an organization’s reputation.

Domain Threat Intelligence as the Core Component of Early Threat Detection

While there are many indicators of cyber attacks, malicious (or likely to become malicious) domains stand out as key early warning signs. Cyber threat actors weaponize domains for various nefarious purposes:

• Phishing campaigns: Threat actors register domains that mimic legitimate websites to trick users into divulging credentials. Example: rnicrosoft[.]com, a typo-variant of microsoft[.]com, was used to send phishing emails.
• Malware distribution: Domains are used to host malicious software for download. When users download free software from info-zoomapp[.]com, for example, they download an installer bundled with malware, such as a keylogger that records their keystrokes or spyware that steals their financial information. 
• Command and control (C2) infrastructure: When malware has successfully infected a computer, it often needs instructions from its operators. So, it’s configured to periodically call home to a random domain, such as depo-govpk[.]com (a domain used by SideWinder in one of its attacks). The C2 domain commands the malware to scan network traffic and endpoints, perform privilege escalation, find and upload financial data, install ransomware, or execute other malicious commands.

To prevent them from succeeding with these tactics, organizations rely on threat intelligence feeds that incorporate IoCs such as malicious domain names and IP addresses. Incorporating these into a security stack helps shift threat detection one step further to the left.

How AI Transforms Early Threat Detection

There’s one invention that helps detect threats even earlier — and that’s AI. Its ability to process vast amounts of data and identify subtle behavioral patterns far beyond human capabilities enables the following:

• Behavioral analytics and anomaly detection: Machine learning algorithms can establish baselines of normal user activities and network traffic behavior. Any significant deviation from these behavioral patterns, even subtle ones, can trigger alerts, indicating potential malicious activity. This is particularly effective against zero-day threats that traditional signature-based detection might miss.
• Predictive threat prevention: AI can analyze historical threat intelligence and emerging attack trends to anticipate and detect cyber threats before they are even executed. This allows organizations to proactively strengthen their defenses against likely future attacks.

Imagine being able to block those domains before they reach target users or networks or even before they are first weaponized. This is exactly what predictive threat intelligence can do. It relies on using AI to analyze millions of domain registration and configuration data points to identify suspicious characteristics and threat patterns. 

Predictive threat intelligence allows security teams and solutions to flag and block a domain the moment it is registered, before it ever sends a single phishing email or hosts a malicious file.

Early threat detection, in that case, would look something like on the diagram above, effectively stopping the threat long before it can proceed to the lateral movement phase, preventing the phishing email from reaching the user's inbox. 

How WhoisXML API Can Help

To enable AI-driven early threat detection, WhoisXML API has developed the First Watch Malicious Domains Data Feed, an AI-driven malicious domain feed designed to be an organization's first line of defense.

First Watch Malicious Domains Data Feed constantly monitors newly registered domains in real time. It automatically analyzes them and selects those that match patterns commonly used by cybercriminals—such as domains created by domain-generation algorithms (DGAs), phishing sites, or domains used by botnets. This allows it to detect potentially dangerous domains right at the moment they’re registered, with a high degree of accuracy.

First Watch uses machine learning to reduce false positives, recently achieving a 1.66% false positive rate (down from 3%), which translates to more than 98% automated threat detection accuracy. This improvement greatly reduces alert fatigue and lowers the number of legitimate domains being blocked.

How exactly can organizations use First Watch Malicious Domains Data Feed? Here are some use cases:

• Integrate with security tools to enhance security operations: First Watch Malicious Domains Data Feed can be integrated with the security operations center’s (SOC) existing Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms for automated defense. The data feed can also be used to automatically update security controls (e.g., firewalls and DNS filters) with the latest domain intelligence.
• Prevent initial access: With First Watch, security teams can identify and block communication with malicious domains within the first hour of registration, effectively stopping attacks before they launch.

Conclusion 

IBM’s research shows that the average cost of a data breach has decreased this year due to faster threat detection and response, highlighting the business benefits of early threat detection. 

AI-enabled solutions, such as WhoisXML API’s First Watch Malicious Domains Data Feed and other predictive threat intelligence feeds, enable security teams to proactively identify and neutralize cyber threats at their earliest stages — at domain registration phase (well before lateral movement), nullifying dwell time and protecting their financial, regulatory, and reputational interests.

Download a First Watch sample file or contact us now to learn more about the First Watch Malicious Domains Data Feed.