Threat Intelligence use cases and examples

Common examples of threat intelligence include:

• Indicators of compromise (IOCs)
• Tactics, techniques, and procedures (TTPs)
• Attacker attribution
• Vulnerability information
• Threat intelligence feeds

When taken separately or used together, they serve as a preventive, proactive approach to securing systems and sensitive data.

IOCs are forensic artifacts that signal potential security incidents in your environment. These include malicious IP addresses, file hashes, domain names, and URLs that help you detect when attackers are present in your systems. When your security tools identify an IOC from a recent attack campaign, you can immediately investigate whether that indicator appears anywhere in your environment.

TTPs describe how threat actors operate and the methods they use to gain access, move laterally, and achieve their objectives. Understanding these patterns helps you recognize attack behaviors even when specific IOCs change. When you know that a particular threat group typically uses spearphishing to gain initial access, you can configure detection rules around these behaviors.

Attribution intelligence identifies which threat groups are behind attacks and reveals their typical targets, motivations, and capabilities. This context helps you assess whether your organization falls within a particular group’s targeting scope. Attribution also helps you understand attacker objectives—whether they’re trying to steal intellectual property, deploy ransomware, or maintain persistent access.

Vulnerability intelligence provides details about software weaknesses that attackers could exploit. This includes CVE data, exploit availability, and information about which vulnerabilities threat actors actively target in the wild. Vulnerability intelligence helps you separate theoretical risks from active threats by showing which flaws attackers use in campaigns.

Feeds deliver continuous streams of threat data from multiple sources, including security vendors, government agencies, and information-sharing communities. These feeds keep your defenses updated against emerging threats without requiring manual research. Feeds vary in focus: some provide broad coverage of many threat types, while others specialize in specific areas like phishing domains or ransomware infrastructure.

Taking a use case-centric approach makes threat intelligence actionable rather than abstract. When you map threat data to specific security functions like incident response and vulnerability management, you can measure its impact on your security outcomes and make better decisions about where to invest resources.

Threat intelligence lets you identify and block threats before they reach your environment. By understanding attacker infrastructure and methods in advance, you can configure your defenses to stop attacks at the perimeter. For example, when intelligence reveals that a threat group is registering domains following specific naming patterns, you can block those patterns in your DNS filtering.

When incidents occur, threat intelligence provides context about what you’re facing. You can quickly determine attack scope, identify related indicators, and understand attacker objectives to contain threats faster. For example, if you detect suspicious PowerShell execution on an endpoint, intelligence about similar attacks tells you what to look for next–whether an attacker typically attempts lateral movement, data exfiltration, or credential theft.

Intelligence guides your search for hidden threats already present in your environment. By focusing on known TTPs and IOCs relevant to your industry, you can uncover sophisticated attacks that evade automated detection. For example, if threat groups targeting your sector commonly use living-off-the-land techniques with administrative tools, you can hunt for anomalous use of those tools.

Applied Threat Intelligence in Google Security Operations goes beyond storing indicators of compromise and adversary behaviors. It uses Google’s understanding of the threat landscape, including Mandiant’s emerging threat intelligence, and applies this knowledge to your specific environment. The system automatically correlates threat data with your security telemetry to identify relevant threats without manual analysis. You get prioritized and actionable outcomes that connect events, alerts, assets, and users into a coherent story so you can stay ahead of the latest threats.

The requirements-driven approach structures how you collect and use threat intelligence based on your organization’s actual needs. This process explicitly meets the specified needs of all relevant stakeholders across the threat intelligence lifecycle. It starts with a threat profile that provides context on the most relevant threats in your sector, industry, and region. Stakeholder analysis produces intelligence requirements and use cases, which inform your collection planning and development of collection assets. These elements combine to form service lines that generate outputs meeting stakeholder requirements, formats, and reporting frequency.

This approach includes several components working together. Planning and direction establishes what intelligence you need and why. Collection gathers raw data from relevant sources. Processing converts that data into structured formats. Analysis transforms processed data into actionable insights. Dissemination delivers those insights to decision-makers and security teams who can act on them. You can apply this framework across multiple use cases, from vulnerability management, to threat hunting, to security architecture planning.

Threat intelligence becomes most valuable when you apply it to your specific environment and security challenges. Google Threat Intelligence combines insights from monitoring over 500 threat actors with automated detection capabilities, giving you prioritized intelligence relevant to your industry and region. By integrating these capabilities into your security operations, you can move from collecting threat data to preventing attacks.

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