How Artificial Intelligence Transforms Threat Detection and Prevention

• Unlike predictive security, traditional cybersecurity is reactive, responding to attacks after they’ve occurred, leading to damage and disruption.
  
  

• AI enhances threat detection by identifying anomalies in network traffic, user behavior, and system logs.
  
  

• PreCrime™ uses behavioral AI to automatically predict and preempt malicious campaigns, stopping attacks before they happen.

In many ways, cyberattacks are like forest fires. Vulnerabilities in a system can make a damaging fire more likely, and when flames do take hold, there’s often significant damage before responders can reach the scene. But what if you could see the spark before the fire ignites?

That’s the promise of AI-based predictive security. AI can anticipate cyber threats, moving beyond simple detection to proactive prevention. BforeAI’s PreCrime™ analyzes vast amounts of data to identify anomalies, predicting attacks before they launch. 

This “early warning system” helps you to harden your defenses, disrupt malicious infrastructure, and ultimately safeguard your valuable assets. It allows you to shift from playing catch-up to staying one step ahead, ensuring business continuity and peace of mind from digital threats.

The Limitations of Traditional Cybersecurity

Traditional cybersecurity is usually characterized by a reactive approach, meaning that it primarily focuses on responding to attacks after they have already occurred. This approach often results in significant damage and disruption with organizations being left scrambling to contain breaches and mitigate losses. 

One of the main limitations of these traditional security methods is their reliance on signature-based detection. They involve identifying unique patterns in the code or behavior of known malware. However, this approach is ineffective against new or evolving threats since it can only flag previously identified malicious patterns. 

If malware changes its code, the existing signature won’t match, and the detection will fail. This makes signature-based detection reactive rather than preemptive.

Another issue is alert fatigue. This occurs when security teams are frequently overwhelmed by the sheer volume of alerts generated by traditional security systems. A 2020 Forrester report indicated that the average security operations center (SOC) team was dealing with approximately 11,000 alerts per day, which is about 450 alerts per hour. 

Many of these alerts are false positives, which further distract security teams from genuine threats. Consequently, a significant number of alerts are left uninvestigated; a 2023 IBM survey revealed that SOC teams investigate only 49% of the alerts they receive on a typical day.

The Rise of AI in Cybersecurity

Artificial intelligence (AI) has emerged as a powerful solution to the limitations of traditional cybersecurity. It can analyze vast amounts of data in real time, identifying patterns and anomalies that would be impossible for humans to detect at scale. This capability allows AI to drastically reduce reaction times, which is critical in today’s fast-paced threat landscape.

AI offers numerous advantages when applied to cybersecurity. Advanced threat detection is enhanced through AI’s ability to identify subtle anomalies in: 

• Network traffic 
• User behavior 
• System logs 

This is complemented by behavioral analytics, where AI learns normal user patterns, such as login credentials and application usage. The data is used to flag deviations in real time to predict threats before they cause harm. 

The reduction of false positives is another key benefit, with AI distinguishing between harmless anomalies and genuine threats, improving the efficiency of your security teams. Continuous monitoring and adaptation are further improved as AI works tirelessly to analyze patterns and gather insights that refine threat-detection capabilities. 

Automated responses to emerging threats also minimize damage and risks in real time. This is because AI can be configured to manage low-level threats autonomously while escalating complex issues for human review. By leveraging these capabilities, AI is transforming cybersecurity from a reactive practice to a preemptive one.

What Is Predictive Security?

AI-based predictive security uses artificial intelligence to anticipate and prevent cyberattacks, shifting the focus from reaction to anticipation. It aims to move beyond simply detecting threats to stopping attacks before they start.

The core principle is to identify threats prior to launch, harden systems, disrupt potential attack vectors, and minimize the risk of successful breaches before they occur. This contrasts with traditional methods that only respond after an attack has already taken place, which is often too late to prevent damage.

A predictive security approach has the following main components:

• Behavioral analysis
• Preemptive action
• Threat prediction  

With behavioral analysis, AI detects patterns and anomalies. It learns normal user and system behavior to identify trends and deviations that may indicate malicious activity. Preemptive action prevents attacks before they happen through tactics such as disrupting malicious infrastructure by communicating with registrars and takedown operators to disable them.

Threat prediction anticipates potential attacks based on the analyzed data. This helps AI algorithms identify suspicious patterns in the data that may indicate an impending attack. 

How PreCrime™ Works

BforeAI’s PreCrime™ platform is a forward-thinking security solution that uses behavioral AI to predict and automatically preempt malicious campaigns. It shifts from a reactive approach to a preemptive one, stopping attacks before they happen.

Our platform starts with data collection to gather network information from thousands of sensors deployed across the internet. It observes more than 1 billion infrastructures and 500 million domains, collecting several terabytes of data daily. 

Next is graph construction, where a graph is constructed by preprocessed data, extracting features such as query frequency, temporal patterns, and resolution paths. This includes the mapping of over 400 billion behaviors and edges in our database.

Once the graph has been constructed, the graph inference process begins. This includes: 

• Anomaly detection 
• Link prediction 
• Community detection 

The inference process serves to identify clusters of domains with ill intent based on four billion different malicious behaviors.

The final step in the PreCrime™ platform is identification and disruption. Based on the graph inference analysis, it detects infrastructures that show characteristics of malicious behavior. The platform flags these infrastructures for further investigation and initiates disruption to immediately disable or degrade the infrastructure until a takedown can be completed.

PreCrime™ analyzes over 70,000 indicators of attack (IoA) daily, connecting through an API to your existing threat intelligence systems. It is designed to be a “plug and play” product and easy to work with. It’s also fully automated, providing a dashboard for ongoing performance metrics and threat monitoring. 

PreCrime™ vs. Traditional Threat Intelligence

PreCrime™ and traditional cyber threat intelligence differ significantly in their approach and focus. Traditional threat intelligence is useful for attributing attacks and for forensics to understand the steps criminals take. 

In contrast, PreCrime™ focuses on preventing attacks by providing predictive insights rather than reactive data. It’s more proactive and effective than traditional threat intelligence by identifying and disrupting malicious infrastructures before they can be used to launch attacks. 

Instead of providing indicators of compromise (IoCs) — signs of an existing compromise — PreCrime™ offers indicators of future attack (IoFAs). They predict attacks with a median identification of 18 days ahead of traditional threat intelligence tools. PreCrime™ also has a faster response time and a lower false positive rate compared to traditional threat intelligence. 

In addition, BforeAI offers the PreCrime™ Guarantee, which reimburses you up to ten times the value of your service contract if impacted by a cyberattack due to a failure of BforeAI’s predictive solution. This guarantee highlights the confidence we have in the effectiveness of our platform.

The Impact of Predictive Security

To put it most simply, predictive security is a more efficient way of dealing with threats because it stops them before they have a chance to impact an organization. It can significantly reduce costs by preventing attacks before they occur. 

This preemptive approach helps maintain business continuity by minimizing disruptions caused by security incidents. Predictive security also contributes to regulatory compliance, as many industry regulations require organizations to implement proactive security measures.