Security Intelligence: An Introduction

From safeguarding sensitive data to detecting and mitigating evolving threats, modern cybersecurity systems must be dynamic and intelligent to keep up with the constantly evolving digital landscape. Achieving this requires a proactive approach that uses real-time data and advanced tools to identify risks and respond effectively.

Security intelligence plays a critical role in transforming raw information into actionable insights that strengthen defense mechanisms and prevent cyberattacks.

In this article, we will dive into the concept of security intelligence, its importance in cybersecurity, and how it integrates with technologies like AI and machine learning to provide protection.

What is security intelligence?

First, let’s define what security intelligence means. Security intelligence refers to the collection, standardization, and analysis of real-time information to improve cybersecurity defense. This information can come from:

• Network nodes and endpoints
• Users
• Applications
• Technology systems

This information can also come from external sources such as:

• Virus signatures
• Vulnerabilities
• Exploits

The process involves acquisition, storage and analysis of real-time data streams. The data may be stored in a centralized repository that integrates third-party analytics and ML tools for security intelligence.

The analysis involves data preprocessing after it is ingested from multiple sources. It is then used to train or guide models that compare the real-time data behavior and trends to a known reference.

The reference behavior of the data streams may also change based on contextual knowledge such as traffic patterns and network health.

(Related reading: network traffic/congestion.)

AI and ML in security intelligence

This is where advanced ML algorithms play an important role: AI models can generalize large volumes of complex and feature-rich data. The generalization is not static.

As new data trends emerge, you can retrain your AI models to adapt and learn the new patterns and trends. This process is fairly autonomous and scalable for large scale networks. When the AI models are sufficiently trained on new data, their view of the reference normal behavior is updated.

The new generalization can now comprehensively serve as an anomaly detection tool against new threats and guide security actions based on real-time knowledge of the system threats facing the IT network.

Business decision makers and security professionals want security tools that are truly intelligent in this sense. There is a lot of hype around Artificial Intelligence in the enterprise IT segment, and rightly so.

(Related reading: AI trust, risk, and security management.)

Adopting AI capabilities into cybersecurity systems

AI adoption is on the rise and vendors are increasingly integrating AI capabilities into their products and services.

But in the context of cybersecurity, what makes a technology truly intelligent?

To answer this question, let’s review some of the most important capabilities and key elements of a cybersecurity technology system that can enable Security Intelligence.

The data pipeline

A security intelligence system is built on an extensive end-to-end data processing and analysis pipeline. It relies on advanced data platforms such as a data lake for real-time data ingestion. A preprocessing pipeline prepares structured, unstructured and semi-structured data for analysis according to standardized tooling specifications.

AI models may be developed in-house to extract insights from raw information. Third-party integrations are used to enhance security functionality, such as anomaly detection, intrusion detection and intrusion prevention.

Real-time data assets

An important feature of security intelligence is that data acquisition, processing and analysis can take place in real-time.

Consider the cyber-attack kill chain: prior to executing a data breach attack, the threat actor spends time monitoring network behavior, attempting to access network nodes and installing malicious payload.

The cyber-attack kill chain has seven steps:

1. Reconnaissance: Attackers gather information about the target using public data, spying tools, and automated scanners to identify vulnerabilities.
2. Weaponization: They develop or modify malware to exploit identified weaknesses.
3. Delivery: The malware is delivered through phishing, social engineering, or hardware/software vulnerabilities.
4. Exploitation: Attackers use the malware to exploit vulnerabilities and gain deeper access to the network.
5. Installation: Malware is installed to establish persistent control, often using Trojans, backdoors, or privilege escalation.
6. Command and control: A remote channel is set up for attackers to guide their actions and obscure their presence.
7. Action: The attackers execute their goal, such as data theft, encryption, or supply chain compromise.

These activities may go under the radar of an individual security monitoring tool, but the logs captured in real-time can be analyzed in context of the wider network behavior. This behavior evolves in real-time and anomalous activities that correspond to data leaks in the future can be identified as anomalous.

Actionable insights

Security intelligence goes beyond traditional monitoring and observability tools. It is designed not just to raise flags when a network parameter exceeds a predefined threshold, but to guide security professionals and cyber defense tools to act optimally based on real-time threat intelligence.

Considering the scale of network operations and the complex nature of sophisticated cyber-attacks, manual intervention may be ineffective and time consuming.

Third party integrations play an important role here: extending functionality and automating actions based on real-time information. This is where standardizations and data processing to comply with tooling specifications is an important part of your data pipeline.

(Read about Splunk’s threat intelligence management.)

SDLC and security policies

Security intelligence is focused on action and behavior of the organization, as much as it is focused on transforming raw data into insights. These insights often point to change in the Software Development Lifecycle (SDLC) approach, culture and project management.

For example, security intelligence may require organizations to improve collaboration between developers and security (think DevSecOps). Or the organization may be encouraged to invest in private cloud data centers instead of relying on legacy servers in-house.

(Related reading: cybersecurity policies.)

The goal of security intelligence

The goal of security intelligence is to establish a mechanism for real-time data-driven decision making on security issues, especially considering the dynamic behavior of the network, applications, network systems and user traffic.

Security intelligence is focused on:

• Reducing the risk posture of your enterprise
• Securing sensitive business information
• Protecting your IP and digital assets
• Reducing the operational efforts and investments to keep your systems secure

Security intelligence is a paradigm that can scale to meet different security needs of all organizations, at different maturity levels of the technology adoption curve.

Nevertheless, a starting point for industry laggards can be the data pipeline that can enable comprehensive and real-time data acquisition. Early adopters and leaders can focus on advanced data analysis tools and proprietary algorithms developed in-house based on their own unique data assets.

To wrap up

In short, security intelligence builds on log management, SIEM, risk management and network forensics technologies. This provides organizations with a comprehensive framework to anticipate, detect, and respond to cyber threats effectively.