How AI Is Changing the Threat Intelligence Process

In the break-neck arms race between cybersecurity experts and bad actors, the prospect of AI threat intelligence is often hyped as a “game-changer” for protecting your digital assets. But what do we really mean when we talk about “automated threat intelligence” and how can you best use it to defend your digital and physical assets in an increasingly hostile environment?

Read on to discover expert insights into the nuances of cyber intelligence and learn how the shift from manual to AI-driven methods will affect modern security operations.

The Evolution of Threat Intelligence

Traditional threat intelligence relies on manual processes—security teams collect data from various sources, then spend days, weeks, or even months sifting through it, often missing critical patterns in massive datasets. Those mistakes lead to false positives that consume valuable resources or allow genuine threats to slip through unnoticed, and the extended gap between threat detection and response leaves organizations vulnerable.

"The legacy work involves a significant number of people conducting manual activities," ZeroFox's Chief Technology Officer Mike Price agrees. "There's just a lot of lift in doing that and they move too slow. It's very hard to keep current."

And this is where AI threat intelligence comes in, although Mike isn’t so keen on using that term.

"To be precise, AI threat intelligence would strictly speak to threats related to AI systems,” he clarifies. “What we’re really talking about here is AI-enabled threat intelligence, that is, threat intelligence systems that leverage AI." 

ZeroFox's Chief Technology Officer is also wary about perpetuating the marketing hype that surrounds threat intelligence automation.

"It's another tool that supports you in performing your task. You're taking regular threat intelligence, or cyber threat intelligence and applying AI, in the same way you might use Word or a text editor although likely to greater effect."

However, even Mike has to admit that AI-enabled threat intelligence allows organizations to achieve dramatic results, not least in terms of speed: "If you're still doing everything in spreadsheets, you're probably talking about days, or weeks, or months to see results. Whereas with automated threat intelligence solutions, like ZeroFox, you're working on timeframes that are days, hours, minutes, or near-realtime." 

Such drastic reductions in time-to-remediation (TTR) are not the only benefits of threat intelligence automation. Let’s take a more expansive look at how AI is transforming the threat intelligence process.

Strategic Advantages of AI Threat Intelligence:

Enhanced Speed and Accuracy with Automated Threat Intelligence

Although AI dramatically accelerates the detection and analysis process through automation, this speed doesn't sacrifice accuracy. Producing the intelligence faster, allows you to be “More proactive, provide better coverage and make fewer mistakes," according to Mike. Because machine learning algorithms excel at pattern recognition, they are better at identifying the indicators of attack—subtle signs of malicious activity that might escape human notice. What’s more, by analyzing vast quantities of historical threat data, these systems learn to effectively distinguish between genuine threats and accidental alarms, minimizing resource-hungry false positives.

AI also provides crucial context for threat assessment. Rather than simply flagging potential risks, AI systems analyze relationships between different indicators, assess source credibility, and evaluate the likelihood of exploitation. This contextual understanding helps security teams prioritize responses and focus resources on the most serious threats.

Comprehensive Visibility Across All Assets

AI technology proves particularly valuable for asset inventory—a critical operation. Even if they have the most comprehensive cybersecurity protecting their internal systems, modern organizations must also defend a diverse array of assets outside their traditional network perimeter, which can seem like a daunting task. 

"These days, if you're in charge of security for a company, you have a hard time tracking what everybody at your company is doing or putting up on the Internet,” Mike observes. 

"But no matter how awesome all the tools you have are, you can't defend what you don't know about, none of it will help.”

Mike explains that ZeroFox categorizes “the entire universe of assets” into three “buckets” to make the job of protecting them more digestible:

• Cyber assets: "This includes all the traditional stuff—IPs, hostnames, domains, websites, and APIs"
• Digital assets: "Roughly speaking, everything else, but especially social media, and the deep and dark web. It also includes your mobile applications and third-party supply chain"
• Physical assets: "ZeroFox provides physical security for some of its customers, so we have another bucket for any kind of real-world assets"

Mike emphasizes the dynamic nature of protecting this universe of assets: "It changes day over day, especially for large companies where people across the business are constantly standing stuff up across websites and social media accounts."

Although this expansion beyond traditional network perimeters creates blind spots that attackers exploit, AI-enabled threat intelligence helps organizations identify new assets almost in real-time. By processing the numerous inputs required to track what employees across the company are deploying on the internet, it provides comprehensive monitoring across these diverse environments, for example by:

• Surfacing activity on social media platforms where brand impersonation and phishing campaigns often originate
• Monitoring code repositories for exposed credentials or vulnerable configurations
• Scanning app stores for malicious applications masquerading as legitimate software

Pattern Detection and Response Capabilities

While cybersecurity marketing loves to describe AI systems as having Minority Report-like "predictive" capabilities, Mike offers a more nuanced view: "It's more like helping to detect and respond to attacks through pattern analysis. Primarily, what we do is identify things that you could describe as indicators of attack," he explains. "For instance, if we see that somebody's created an impersonating social media profile, or they’ve set up a phishing domain, we know this is something bad actors do as part of an overall attack."

By correlating these indicators with known threat actor behaviors, AI systems can actually help security teams understand not just what is happening, but what might happen next based on historical patterns.

This capability enables proactive defense through threat mitigation rather than just reactive countermeasures. Organizations can patch vulnerabilities before exploitation, strengthen authentication on targeted accounts, and prepare incident response teams for specific attack scenarios. For example, ZeroFox can rapidly deploy threat mitigation actions such as automated takedown procedures or adversary infrastructure disruption, or, as Mike puts it, “We identify the threat, and deal with it.”

Transforming the Threat Intelligence Lifecycle with AI

The threat intelligence lifecycle is a structured process that organizations use to transform raw data into useful security insights. This cycle—traditionally consisting of planning, collection, processing, analysis, dissemination, and feedback—has remained fundamentally unchanged for decades. AI doesn't replace this proven framework; instead, it updates how each phase operates, turning what were once manual, time-intensive tasks into automated, continuous processes.

Let’s take a closer look:

Planning and Direction

While traditional planning often relied on generic industry reports and historical incidents, leading to misaligned priorities, AI enhances the planning phase by helping organizations automatically develop more effective intelligence requirements.

Threat intelligence automation analyzes an organization's specific threat landscape, considering industry, geography, technology stack, and business model. It identifies which threat actors are most likely to target the organization and what methods they typically employ. This analysis helps security teams focus intelligence gathering on the most relevant threats.

Collection and Processing

Automated threat intelligence technologies easily handle the scale and complexity required in modern threat intelligence collection. The systems continuously scan vast areas of the internet, from public websites and social media to password-protected dark web forums and encrypted messaging platforms.

They parse different data formats, translate foreign languages, and extract relevant information from images and videos.

Processing collected data presents another challenge that AI handles efficiently. Raw intelligence comes in many forms—structured threat feeds, unstructured text, network logs, and more. AI normalizes these diverse inputs into standardized formats for analysis. It enriches raw data with context from threat intelligence databases, adding details about known threat actors, their techniques, and typical targets.

Analysis and Production

In the analysis phase, AI excels at identifying complex patterns and relationships. Machine learning algorithms identify correlations between seemingly unrelated events, revealing coordinated campaigns that might otherwise appear as isolated incidents. Natural language processing extracts insights from threat actor communications, technical documentation, and security research.

AI can then produce various forms of intelligence output, from technical machine-readable intelligence to comprehensive PDF reports. The technology generates appropriate intelligence products for different audiences—executive briefings with strategic insights for leadership, and tactical details for security operations teams.

Dissemination and Response

AI streamlines intelligence distribution through intelligent routing and automated sharing mechanisms. The technology understands which threats are relevant to specific teams or systems and ensures alerts reach appropriate recipients without overwhelming them with irrelevant information.

Machine-to-machine intelligence sharing enables immediate protective action. AI systems automatically share threat indicators with your SIEM, SOAR, TIP, or IAM security tools through standardized protocols and APIs. This integration allows firewalls to update rules, endpoint protection to block malicious files, and authentication systems to flag compromised credentials, all without human intervention.

Mike completes the picture: "And then on the far side, you have an operations team that consumes the intelligence and responds as needed."

When discussing threat response, Mike prefers the term "disruption" over takedown: "Of course, we work to take threats down, but we also work with third parties to degrade the attackers and disrupt whatever capabilities that they're leveraging, including their wider infrastructure."

And in the bigger picture, a feedback loop drives continuous improvement as AI systems learn from outcomes, analyst feedback on alert quality, and the effectiveness of recommended responses. This learning process refines algorithms over time, improving accuracy and reducing false positives ever further.

ZeroFox's Approach to AI Threat Intelligence

ZeroFox takes a universal view on asset protection and helps organizations defend everything across cyber, digital, and physical asset categories. Here’s how ZeroFox blends automated threat intelligence technology with human expertise to protect organizations from external threats:

The ZeroFox Platform: Comprehensive Protection Beyond the Perimeter

The ZeroFox External Cybersecurity Platform integrates multiple AI-driven capabilities for comprehensive protection beyond traditional perimeters. The platform continuously monitors the external attack surface, gathering intelligence from surface, deep, and dark web sources. When threats are detected, ZeroFox activates disruption capabilities to deal with immediate dangers and prevent future attacks.

ZeroFox Intelligence Feeds: Purpose-Built for Real-World Threats

ZeroFox's Intelligence Feeds deliver threat data directly to your existing security platforms, enhancing their effectiveness without requiring infrastructure replacement. The feeds provide comprehensive coverage with historical context and current intelligence, maintain high accuracy through advanced filtering and validation, and deliver timely updates through direct machine-to-machine connections.

OnWatch Managed Intelligence Services: Addressing Regulatory Concerns

ZeroFox's OnWatch managed intelligence services provide an important option for organizations more wary about AI usage, particularly in financial services and regulated industries. With OnWatch, you can be sure that human analysts check and validate every alert 24/7, providing highly predictable alerting according to standard operating procedures. This human-in-the-loop approach gives organizations the benefits of AI-enabled threat intelligence while maintaining the oversight necessary for regulatory compliance. 

The Future of AI Threat Intelligence

As cyber threats continue evolving, AI-enabled threat intelligence will become increasingly critical to deal with threat actors using their own AI tools: "We expect attackers to get more efficient on their side,” Mike says. “So, as defenders, we all need to get more efficient on ours."

While AI excels at data processing and pattern recognition, human analysts provide the strategic thinking and contextual understanding. "AI-enabled threat intelligence means we can ask better questions, look in better places, do better analysis, and ultimately, produce better outcomes.”

"And that means ZeroFox can help you defend everything that you need to defend, there's no caveat to that," Mike concludes.

Ready to transform your threat intelligence capabilities with AI? Get a demo today to see how ZeroFox can help your organization stay ahead of evolving threats.