Executive Summary

The cybersecurity landscape is dominated by reactive defenses. Security teams constantly chase indicators of compromise (IOCs), responding to threats only after an attack has already taken place. But what if organizations could predict attacks before they happen? 

The Science Behind Predicting Cyber Threats introduces a significant shift in threat intelligence, explaining how Augur, an AI-driven predictive threat intelligence platform, enables organizations to block cyber threats before they become active.

This paper explores why traditional security models fail to keep pace with rapidly evolving cybercriminal tactics, the science behind Augur’s behavioral analysis at scale, and how machine learning models can anticipate malicious activity before it materializes into an attack. 

Readers will learn how predictive threat intelligence reduces risk, eliminates alert fatigue, and enhances operational efficiency, providing an essential advantage in today’s high-stakes cybersecurity environment.

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The Problem with Traditional Threat Intelligence

Today’s cybersecurity is fighting yesterday’s battles. 

Imagine a security guard whose only job is to investigate past break-ins. Instead of stopping burglars before they strike, he only responds after a break-in happens — collecting evidence, reviewing footage, and filing reports.

That’s precisely how most cybersecurity works today.

The problem? Traditional security models are too slow and require too much human effort.

Attackers don’t just evolve, they scale. With advances in AI, cloud infrastructure, and automation, threat actors can launch and refine attacks faster than security teams can respond. By the time an indicator of compromise (IOC) is detected, it’s often too late. Hundreds or thousands of victims have already been compromised.

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• Attackers move faster than defenders can respond. Leveraging AI, cloud-based automation, and scalable attack infrastructure, adversaries can target thousands of victims before security teams even register the first signs of compromise.
• Indicators of Compromise (IOCs) arrive too late to matter. The first detection of an attack — patient zero — is already outdated. By the time it’s shared in a threat feed, patients 1 to 1,000 have already been breached.
• Threat actors may leave attack infrastructure dormant for years. While some attackers rotate assets, many maintain infrastructure for months or years to avoid suspicion. Traditional security tools struggle to identify these long-lived threats until the damage is already done.
• Security teams end up playing whack-a-mole, but the game is rigged. Reactive threat intelligence forces defenders to chase yesterday’s attacks while adversaries continue their campaigns undetected.

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This reactive approach leaves organizations exposed. Even the most sophisticated security tools can only respond after an attacker has already gained access, planted malware, or stolen data.

What would it be worth if we could predict threats before you’re attacked? Attackers don’t just wake up one day and launch an attack. They prepare. They acquire IP addresses, register domains, set up command-and-control servers, and test exploits weeks or months in advance.

If you could detect these behaviors early, you could block threats before they become active. This is what Augur does. Instead of looking at past attacks, Augur detects the early signs of future cyberattacks, allowing organizations to block threats before they even begin.

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The Foundation of Augur: Behavioral Analysis at Scale

Hackers don’t appear out of nowhere. They leave a trail.

Cybercriminals don’t just wake up and launch an attack. Every cyberattack starts with preparation by setting up the infrastructure needed to carry it out. This setup phase is where Augur finds them.

Think of it like catching bank robbers before they strike. Imagine a group planning a bank heist. Before they commit the robbery, they need to:

• Steal or rent getaway cars.
• Buy disguises and tools.
• Scope out the bank’s security measures.

If law enforcement monitored these preparations, they could predict the heist before it happened and arrest the criminals before they even stepped inside the bank.

That’s exactly how Augur works in cybersecurity. Instead of waiting for an attack, Augur detects cybercriminals setting up their attack infrastructure and blocks them before they can act.

How Augur Tracks Cybercriminals Before They Attack

Hackers need digital resources to launch an attack: servers, domains, and networks. Augur monitors massive amounts of internet activity to detect when these resources are being prepared for malicious use.

Augur analyzes four key signals to detect attack infrastructure:

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By continuously tracking these early warning signs, Augur can identify malicious infrastructure before it is used in an attack.

The Shift from Reactive to Proactive Security

Most cybersecurity solutions wait until a threat becomes active, detecting attacks only after they begin. Augur flips the script:

• Instead of waiting for malware to spread, Augur detects it before it’s deployed.
• Instead of responding to a ransomware campaign, Augur identifies and blocks the attacker’s infrastructure before it is launched.
• Instead of playing defense, security teams can use Augur to block threats before they can act.

This preemptive approach makes Augur different, and it’s what allows organizations to stop cybercriminals before they ever make a move.

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The Machine Learning Process: How Augur Turns Data into Predictions

The internet is an ever-changing landscape, with billions of connections happening at any moment. Cybercriminals blend into this noise, setting up their attack infrastructure in ways that mimic legitimate activity.

The challenge? Detecting the difference between a harmless new web server and an attacker preparing to launch a ransomware campaign.

This is where machine learning comes in. Augur processes massive amounts of data, identifies hidden patterns, and accurately predicts future cyber threats.

How Augur’s AI Works

Augur’s predictive threat prevention platform follows a machine learning pipeline that turns raw internet activity into early warnings for cyber threats.

Step 1: Data Collection —Gathering the Signals of Future Threats

Augur continuously monitors global internet activity, ingesting terabytes of data daily from:

• IP and network changes: Who owns which addresses, and how are they being reassigned?
• Domain registrations: new domains that might be linked to malware, phishing, or command-and-control infrastructure
• DNS queries and BGP routing shifts: how attackers configure their domains and networks before launching attacks
• Threat intelligence feeds: comparing new data against known attacker behaviors

This process is similar to watching a city’s traffic patterns, spotting new roads, unexpected detours, and suspicious changes that could signal an impending event.

Step 2: Pattern Recognition & Clustering —Identifying the Unknown

At this stage, Augur’s AI shifts from data collection to analysis. Most threat intelligence platforms rely on known indicators of compromise (IOCs). Augur goes further, detecting threats that have never been seen before.

• Augur doesn’t need an attack to happen first. It identifies patterns that indicate malicious intent.
• Using unsupervised machine learning, Augur groups similar behaviors into "threat clusters.”
• If a new cluster behaves like previous attack infrastructure, Augur flags it as a high-risk entity, even if no attack has been recorded.

Example: If Augur detects a block of IP addresses behaving similarly to those used in previous ransomware attacks, it will proactively add them to a blocklist before other security tools have flagged them.

Step 3: Supervised Learning for Threat Attribution —Connecting the Dots

Once Augur has identified a potential threat cluster, it applies supervised learning to refine its predictions.

• Augur compares new threat clusters against historical attack data to determine the likelihood that they’re malicious.
• It analyzes attacker behavior, linking new cybercriminal activity to known threat groups (e.g., ransomware gangs, nation-state APTs).
• The more Augur learns from confirmed threats, the better it detects new ones.

Think of it like facial recognition for cybercriminals. Even if the hacker uses a different name (or in this case, a different set of IPs or domains), Augur recognizes their methods and patterns, identifying them before they strike.

Step 4: Identifying Malicious Infrastructure and Blocking Threats —Taking Action

Augur doesn’t generate reports, it drives action. Unlike traditional security tools that flood security teams with low-confidence alerts, Augur delivers high-fidelity predictions that can be trusted and acted upon in real-time. 

• Firewalls and EDR systems enable automated, proactive blocking before attackers can engage. 
• SIEM and SOAR platforms enrich security logs with high-confidence predictions to improve correlation and triage.
  Threat intelligence feeds strengthen the broader security ecosystem with verifiable, preemptive threat data.

The difference? Augur’s predictions are designed for action. With a false positive rate under 0.01%, organizations can confidently integrate Augur into their security stack without worrying about introducing noise or unnecessary disruptions. 

• Average early warning time: 51 days before conventional tools detect attacks.
• False positive rate of less than 0.01%: Security teams can trust the intelligence and orchestrate automated blocking.
• Proven track record: Augur successfully predicted attacks like SolarWinds, Colonial Pipeline, MOVEit, and Log4j months in advance.

The Augur Advantage: AI That Predicts Instead of Reacts

Traditional security tools wait for an attack to happen before they act. Augur moves first.
Instead of relying on past threats, Augur predicts future ones, keeping organizations ahead of attackers.

That’s not just threat detection—it’s real threat prevention.

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How Augur Ensures Accuracy and Trust

Predictive threat intelligence is a bold claim. Any security solution that blocks threats before they happen must be incredibly accurate. Otherwise, organizations risk blocking legitimate traffic or missing real threats.

Augur doesn’t just make predictions, it proves them. By continuously validating its intelligence against real-world cyberattacks, Augur maintains a 97% accuracy rate with a false positive rate under 0.01%.

How Augur Ensures Accuracy in Its Predictions

Step 1: Third-Party Confirmation

After Augur flags an IP, domain, or network range as a threat, it continuously cross-checks its predictions against:

• More than 90 global threat intelligence feeds (commercial, open-source, and government sources).
• Security telemetry from customers, validating whether blocked threats attempted real-world attacks.
• Dark web and underground intelligence, tracking cybercriminal chatter to correlate predictions with known attack campaigns.

If another source confirms a prediction later, it reinforces Augur’s model.

Step 2: Tracking Real-World Attack Timelines

Augur doesn’t just predict threats, it tracks how long it takes for those threats to be recognized by the broader security community.

MOVEit Ransomware Attack

• March 2022: Augur detects and blocks malicious infrastructure linked to the attack, 14 months before it was used.
• May 2023: MOVEit is exploited in a significant breach, affecting hundreds of organizations.
• After the fact, third-party security researchers confirm that the attackers used IPs Augur had already flagged and blocked.

This cycle repeats across multiple high-profile attacks, proving that Augur isn’t just detecting known threats — it’s anticipating and stopping future ones before anyone else sees them.

Step 3: Auto-Correction and Continuous Model Improvement

Even with high accuracy, Augur continuously fine-tunes itself: 

• False positives: If an IP flagged as malicious never engages in malicious behavior, Augur will eventually remove it from blocklists.
• False negatives: If an attack slips through and is later confirmed as malicious, Augur adjusts its model to catch similar threats in the future.
• Threat attribution adjustments: Augur refines its ability to correlate infrastructure to specific threat actors, helping organizations understand who is targeting them.

The result? Augur gets smarter over time, reducing noise for security teams while ensuring critical threats are stopped.

Building Confidence in AI-Driven Security

Many security leaders hesitate to automate cybersecurity decisions because they fear false positives.

• Augur eliminates the guesswork; it doesn’t just predict, it proves its accuracy.
• High-confidence, accurate predictions with third-party validation give security teams confidence in blocking threats before they materialize.
• Less noise means better results. Augur delivers actionable intelligence, not just more alerts.

Augur doesn’t use AI just for the sake of AI. This is AI that actively protects organizations from being the next victim.

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Augur vs. SolarWinds, MOVEit, and Log4j

Many security solutions claim to provide "advanced threat intelligence," but few can demonstrate clear, verifiable proof that they predicted and stopped attacks before they were widely known.

Augur can.

Over the past several years, Augur has correctly identified and blocked attack infrastructure weeks or months before major cyber incidents. Let’s look at three of the most high-profile attacks that Augur stopped before they became global crises.

SolarWinds: a Nation-state Supply Chain Attack

The Attack

• In late 2020, the SolarWinds supply chain attack compromised U.S. government agencies and Fortune 500 companies.
• Russian threat actors inserted a backdoor into SolarWinds’ Orion software, impacting 18,000+ organizations.
• This attack went undetected for months before being discovered in December 2020.

How Augur Predicted It

• Augur detected suspicious activity across a block of IP addresses associated with the campaign six months before the attack.
• Augur’s AI flagged 5.252.177.0/24 as high-risk, predicting that it would be used for malicious activity.
• Customers using Augur had already blocked this infrastructure before SolarWinds was publicly identified as a breach.

The Result

• While the rest of the cybersecurity industry scrambled to investigate, patch, and recover, Augur customers had already been protected for months.
• The attack was one of the most sophisticated nation-state operations in history, but Augur outpaced the adversaries by identifying their infrastructure before it was used.

MOVEit Ransomware—14 Months Advance Protection

The Attack

• In May 2023, attackers exploited a zero-day in MOVEit, a popular file transfer tool.
• The attack compromised hundreds of businesses and government agencies, leading to massive data breaches.
• Most security teams were blindsided, reacting only after they had been compromised.

How Augur Predicted It

• In March 2022 — 14 months before the attack — Augur flagged malicious IPs linked to the cybercriminal group behind the breach.
• These IPs were automatically blocked for Augur customers, preventing communication with MOVEit-exploiting infrastructure.

The Result

• Organizations using Augur never had to scramble for emergency patches, isolate infected machines, or report data breaches.
• They were never targeted in the first place.

Log4j: The Internet’s Worst Zero-Day Exploit

The Attack

• In December 2021, a critical vulnerability in Log4j (CVE-2021-44228) allowed attackers to execute code on millions of devices remotely.
• This vulnerability was described as “one of the worst security flaws in history.”
• Once disclosed, attackers exploited it within hours, leaving organizations little time to react.

How Augur Predicted It

• Augur flagged infrastructure linked to Log4j-related exploit activity three months before the attack, before the vulnerability was publicly known.
• Augur blocked these high-risk IPs, ensuring that its customers were already protected when the exploit was revealed.

The Result

• Organizations relying on traditional threat intelligence had to rush to patch vulnerabilities, deploy emergency fixes, and contain breaches.
• Augur customers had already neutralized the risk before the public knew it existed.

Proof that Predictive Security Works

The cybersecurity industry is full of hype, but Augur has repeatedly demonstrated real-world predictive power:

• SolarWinds: predicted six months in advance
• Colonial Pipeline: predicted 13 months in advance
• MOVEit: predicted 14 months in advance
• Log4j: predicted three months in advance

This isn’t theoretical, it’s verifiable, operational proof. Most security tools wait for an attack to be discovered and reported. Augur moves first, blocking threats before the cybersecurity community even recognizes them.

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The Business Impact of Predictive Security

Cybersecurity teams are drowning in alerts and attacks. Ask any CISO, SOC analyst, or security engineer, and they’ll tell you the same thing: There are too many threats, too many alerts, and insufficient time.

Today’s security teams face three significant problems:

• Alert fatigue: SOC teams receive thousands of alerts daily, most of which are low-value or false positives.
• Too many tools, not enough clarity: Traditional security stacks rely on dozens of disconnected threat feeds, requiring manual correlation.
• Reactive vs. proactive security: Most security teams are forced to respond to threats after the fact, constantly playing catch-up.

The problem isn’t that security teams aren’t working hard enough, it’s that they’re using tools built for a reactive world. What if security teams could stop wasting time on past threats and instead prevent future ones?

How Augur Changes the Security Workflow

Instead of chasing threats, security teams using Augur get ahead of them.

Before Augur: The Reactive Model

1. A cyberattack happens.
2. Threat feeds eventually detect it and share IOCs (Indicators of Compromise).
3. Security teams scramble to analyze logs, identify exposures, and respond, often weeks or months after the initial breach.

With Augur: The Predictive Model

1. Augur detects attackers setting up their infrastructure before the attack happens.
2. Threats are automatically blocked before they can reach the organization.
3. Security teams spend less time reacting and more time strengthening their defenses.

The difference? Prevention instead of firefighting.

The Business Impact: What Predictive Security Delivers

CISOs and security executives don’t just need security, they need security that delivers measurable business value.

Fewer Alerts, Less Noise

• Augur makes predictions with a less than 0.01% false positive rate, meaning fewer unnecessary alerts.
• This significantly reduces SOC workload, helping teams focus on real threats.

Lower Incident Response Costs

• The average cost of a cyber breach is now $4.5 million, not including lost business and reputational damage.
• By stopping attacks before they start, Augur helps organizations avoid the financial disaster of a breach.

Faster Security Operations

• Security teams no longer need to triage threats that have been neutralized manually.
• Augur automates early detection and prevents threats from ever reaching an organization’s network.

Stronger Cyber Resilience and Compliance

• Augur helps organizations meet compliance mandates by proactively securing assets before vulnerabilities are exploited.
• This strengthens NIST, ISO, GDPR, and industry-specific compliance frameworks.

Augur is More than Just Another Threat Feed

Traditional threat intelligence gives security teams a rearview mirror. Augur gives them a GPS that helps them avoid attacks before they happen. Instead of relying on threat feeds that react to yesterday’s attacks, Augur helps security teams prevent tomorrow’s.

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Augur as a Security Force Multiplier

Security teams have been stuck reacting to threats for too long, constantly responding to alerts, investigating breaches, and patching vulnerabilities after being exploited.

The problem? Reactive security isn’t enough anymore.

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• Attackers move too fast, deploying zero-day exploits before traditional security tools can detect them.
• Security teams are overwhelmed with alerts and distracted by noise instead of stopping real threats.
• Breaches are costly, with the average ransomware attack cost exceeding $4.5 million, not including downtime, lost business, and compliance fines.

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The solution? Stop waiting for attacks. Start predicting them.

Augur is the First Move in Cybersecurity

Augur flips the cybersecurity model on its head, from reactive to proactive.

• It doesn’t just detect threats, it predicts them.
• It doesn’t just investigate attacks, it blocks them before they happen.
• It doesn’t just respond to incidents, it prevents them from occurring in the first place.

For security teams, that means:

• Fewer breaches: lower risk and compliance costs
• Fewer alerts: less SOC fatigue and faster security operations
• Faster response: blocking infrastructure before attackers can use it

Augur is Not Just a Tool, It’s a Security Force Multiplier

With Augur, organizations move from playing defense to playing offense. Security teams can focus on stopping tomorrow’s attacks instead of cleaning up yesterday’s breaches. It’s not just threat detection, it’s threat prevention at scale.

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Are you ready to make the first move? 

The future of cybersecurity is predictive. Don’t wait for the next breach to prove it.

• Request a demo to see how Augur’s predictions work in real-time.
• Learn how your organization can integrate Augur’s predictive threat prevention today.

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