What Preemptive Cyber Defense Is (and Isn’t)

Preemptive cyber defense (PCD) uses artificial intelligence, machine learning, and advanced data analytics to identify and neutralize cyberattacks in their early stages. By identifying trends and anomalies linked to known threats, PCD enables the organization to minimize the threat that they pose to the business.

PCD differs from proactive security and responsive security due to its use of automation to disrupt in-progress attacks. In contrast, proactive security focuses on closing security gaps that an attacker could exploit, while traditional, reactive security centers on the detection and remediation of in-progress attacks.

This article explores how PCD works, including its core components and some common use cases for PCD solutions. It also offers suggested best practices for implementing PCD in a way that reduces risk and enhances an organization’s security posture.

Core Components of PCD

The objective of PCD is to take data from various sources, use it to predict potential threats to the business, and automatically respond to these threats, neutralizing them before an attack can be executed. This process is broken up into the following three stages.

#1. Data Ingestion & Enrichment

Preemptive cyber defense uses multi-source data to gain insight into potential future attacks. PCD solutions may collect data from:

• Threat intelligence feeds
• System logs
• Network traffic
• Endpoint security systems

This information is enriched with other data to turn it into actionable intelligence to identify trends and predict attacks. For example, internal data may be combined with information on asset criticality and external threat intelligence to identify likely attacks against high-value assets.

#2. Behavior Prediction Engines

Behavior prediction engines use artificial intelligence and machine learning for trend analysis, anomaly detection, and threat identification in data. The information collected in the previous step can be combined with information on known attack techniques – like that included in the MITRE ATT&CK framework – to identify early signs of attack campaigns.

Behavior prediction engines constantly ingest data and perform additional training throughout their entire lifecycle. With access to more data and additional context about the organization and the roles of various assets within it, these engines can more accurately identify signs of malicious intent and an attacker’s intended objectives and next moves.

#3. Automated Countermeasures

PCD is focused on identifying and blocking attacks before they happen. For this to be possible, it needs the ability to respond quickly to a detected threat. Manual processes can take minutes or hours to complete, while an automated attack can fulfill its objectives in seconds.

PCD systems use deception, denial, and disruption to stop an attack from execution. With the ability to automatically take action to manage an identified threat, these systems reduce the time that an attacker might have access to an organization’s systems.

PCD Use Cases

PCD offers the potential to detect attacks in their early stages and automatically remediate them before they pose a real threat to the business. To do so, they need a clear understanding of how to identify a potential threat and what to do to address one. 

These are some of the most common and impactful use cases for PCD tools:

Ransomware Campaign Prediction

Ransomware is a common threat with a well-known playbook. Some of the common steps in a ransomware campaign include:

• Gaining initial access to an environment through phishing, compromised credentials, vulnerability exploitation, or similar means
• Performing internal reconnaissance and moving laterally through the network to high-value targets
• Installing and executing the ransomware malware on these critical assets
• Identifying and exfiltrating high-value data to be held for ransom
• Encrypting this high-value data as well

PCD tools can identify the hallmarks of this type of attack campaign, enabling an organization to halt it before significant damage is done. For example, analysis of network traffic might identify the initial access mechanism, the attacker’s attempts to map the internal network to find ideal targets, or exfiltration of large volumes of stolen data. This information could be correlated with endpoint data regarding unusual processes or file access patterns associated with the data theft and encryption.

Insider Threat Detection

Insider threats can include various potential risks to the business, ranging from disgruntled employees to a compromised account. These threats are often difficult to detect since the attacker doesn’t need to exploit vulnerabilities to achieve their goals.

PCD can identify signs of an insider threat based on anomalies in user behavior. AI/ML can analyze user actions and identify potential deviations from their normal behavior. These actions can be flagged for analysis, especially if they are the type of high-risk action needed to fulfill an attacker’s goals, such as accessing large volumes of sensitive company or customer information.

Zero-Day Exploit Forecasting

By definition, zero-day attacks take advantage of unknown vulnerabilities. If an attacker discovers and exploits a vulnerability before a patch is publicly released, then an organization has no means of scanning for or remediating the issue in advance.

However, this doesn’t mean that these attacks are completely undetectable. PCD can identify these attacks via their effects, looking for anomalies and patterns that signal an attack in progress. When combined with global threat intelligence, this information can enable these systems to determine that the organization is under attack and automatically deploy countermeasures to mitigate or remediate it.

Implementation Tips

Designed and implemented correctly, PCD has the potential to dramatically reduce an organization’s risk exposure and the cost of cyberattacks by enabling the business to prevent these incidents rather than responding to attacks in progress. 

Some tips for implementing effective PCD include:

• Carefully Consider Data Sources: PCD systems are only as good as the data that is fed into them. When selecting data to collect and enrich, it’s important to ensure that high-quality, relevant data sources are included, while others are excluded to prevent legitimate signals from being buried in the noise.
• Combine Prediction and Automated Response: Prediction may provide early warning of a threat, but this is of little value if a reliance on manual processes means that remediation is delayed for hours or days. Combining prediction with automated response ensures that threats are addressed as soon as they have been detected.
• Select High-Priority Use Cases: Different threats may require various data sources and need tailored response actions. Focusing on likely and high-risk use cases, such as ransomware or insider threats, maximizes the potential impacts of PCD on risk exposure.
• Iterate and Improve: Top cyber threats will evolve over time, and so will best practices for identifying and mitigating them. Regularly reviewing and enhancing PCD tools is essential to maximize their benefits to the business.

Implementing PCD with IONIX

The rise of AI and automated attacks means that organizations are faced with growing numbers of highly sophisticated cyberattack campaigns. These threats make responsive security increasingly ineffective and mandate preemptive cyber defense to defend the organization at scale.

The IONIX platform enables organizations to identify and block attacks by providing insight into their real-world attack surface from the attacker’s perspective. Continuous scanning and automated attack simulation enable security teams to identify the security gaps in their environment that are most likely to be targeted by an attacker and that pose the biggest risk to the business. 

Learn more about reducing your attack surface with IONIX by signing up for a free demo.