What are the Latest Trends in Threat Intelligence Analytics?

Threat intelligence is a cornerstone of modern cybersecurity. As advancements in artificial intelligence (AI) and increasingly complex cyberattacks transform the cyber threat landscape, it’s never been more important to understand attackers' motives, techniques, and targets. Threat intelligence provides this actionable information, helping organizations protect themselves in a rapidly evolving threat landscape. 

However, threat intelligence itself is in a constant state of flux. New tools, techniques, and trends are reshaping threat intelligence, and understanding these developments is vital to staying safe from cybercrime in the years to come. With this in mind, this blog will explore the trends shaping modern threat intelligence analytics.

Integration of AI and Machine Learning in Threat Analysis 

AI and machine learning (ML) transform threat analysis, allowing organizations to process and analyze vast amounts of data at unprecedented speeds. For example, Amazon uses AI in its GuardDuty offering to continuously monitor a cloud service provider’s accounts, activity, and workload for suspicious anomalies. 

Besides offering the ability to collate and analyze data at scale, incorporating AI and ML into your threat analysis process can result in:

• Better accuracy | Automated AI/ML-powered monitoring and detection does not fatigue, miss steps, or fall victim to human error. 
• Faster detection | By scanning repositories, databases, and even data lakes, AI and ML can look for patterns and detect things that deviate from the baseline far faster – and more comprehensively - than a team of human analysts. 
• Actionable insights | Any actionable conclusions can only be drawn by vetting a meaningful sample size. AI and ML-based tools can cover the necessary amount of “ground” in the cloud, autonomously hunt down threats to eliminate false positives and provide insights that SOC teams can act on. 

Automation of Threat Detection and Response

Because of their force-multiplying capabilities, there is a growing reliance on automated tools to speed up threat identification and mitigation. This reduces the need for manual processes, freeing up resources for strategic tasks like planning, real-time decision-making, and optimizing existing controls. Today’s cybersecurity specialists are in high demand. Organizations should invest their staffing budget in experts who can perform the strategic tasks technology can’t—and leave the menial stuff to the machines.

For example, consider IBM’s approach to understanding attackers’ tactics for effective response. Their platform, IBM X-Force, seeks to understand attacker tactics by analyzing malware samples, categorizing behaviors against the MITRE ATT&CK framework, investigating emerging threats, and monitoring vast quantities of cybersecurity data across the globe. 

AI and ML-based conclusions are only as “smart” as the data pool they draw from. The better the data pool, the better the results. By automatically scanning pools of global cyber resources, AI tools have access to the data necessary to spot patterns and draw conclusions. Moreover, machine learning algorithms allow these tools to improve accuracy over time. 

The Rise of Proactive Threat Hunting 

Proactive threat hunting is the practice of spotting potential malicious indicators of attack before they even trigger an alert. It is the art of being on the offensive rather than the defensive side of security. Rather than waiting for a potential surprise, being caught off guard, and engaging in a cyber fight, proactive threat hunting puts the ball in the court of security analysts, who can search for clues at their leisure and eliminate potential sources of threat before they become a problem. 

Proactive threat hunting is crucial to preemptively identifying network threats and is especially important in early detection and neutralizing advanced persistent threats (APTs). The M-Trends 2024 Special Report states that “an area where we have yet to see substantial adoption, yet has the potential to yield significant gains, is in the field of proactive security and red team assessments,” citing how things like platform logs could be used to record messages or URLs between users and provide insights which could expose potential threats ahead of time. 

One real-world example of the attitude that underscores proactive threat hunting and defense is Barracuda’s 2023 move to immediately replace all compromised Email Security Gateways (ESGs) following a zero-day finding, regardless of patch level. 

Knowledge Graphs and Large Language Models (LLMs) in Analytics

Another way to proactively advance threat intelligence is with knowledge graphs and Large Language Models (LLMs). These tools extract structured insights from unstructured data and have the potential to improve the precision of threat intelligence.

Knowledge graphs create a “duplicate” mapping of your enterprise that displays a holistic view of all data within your environment. By organizing data into a network of connected entities, organizations can see the relationships between them, better spot vulnerabilities and weak points, and bolster them before attackers leverage them for compromise. 

Additionally, LLMs amplify cyber threat intelligence in the following ways:

1. Historical trends | LLMs can analyze historical data to spot previous malicious trends that may have a bearing on future attacks. 
2. Threat actor profiling | Using a large amount of data, LLMs can build a profile of threat actors likely to attack the organization, including their tactics, techniques, procedures, motivations, and infrastructure.
3. Knowledge sharing | LLMs provide a centralized threat intelligence platform that facilitates knowledge sharing, cooperation, and collaboration among security analysts. 

Increased Collaboration and Information Sharing

Threat actors recycle their tactics and use them against different companies, countries, and industries. Organizations can learn much from the experience of other defenders who have faced similar attacks. If the attacks were different, they could leverage that information to better prepare for what would have otherwise been the unknown.

That is why the importance of collaboration among organizations to combat cyber threats effectively cannot be understated. Shared intelligence enhances the collective understanding of the threat landscape. It allows every entity to share their piece until the complete picture of secret threat actor workings slowly begins to come to light. 

To this end, Deloitte supports and encourages collaborative approaches in cybersecurity strategies. In the 2024 Deloitte-NASCIO Cybersecurity Study, they advocate for stakeholders to “collaborate to modernize threat response,” stating that “too often, state CISOs are fighting emerging threats with outdated legacy tools and systems. CISOs should look to collaborate with public and private sector tech leaders to help modernize the approach to threats.”

Staying Ahead of Cyber Threats: Making a Measurable Impact

Modern threat intelligence programs are about turning overwhelming amounts of data into clear, actionable insights. By using AI, increased collaboration, and tools like knowledge graphs, teams can spot patterns, uncover risks, and act faster—before attackers gain the upper hand. Combined with proactive threat hunting and smarter collaboration, these approaches help move security operations from chasing alerts to staying ahead of the curve.

To see how a modernized threat intelligence program can cut through the noise and strengthen your defenses, check out Cyware’s Threat Intelligence Platform (TIP).