#18: The Types of Threat Intelligence in Cybersecurity [7-min read]
Exploring #FrontierAISecurity via #GenerativeAI, #Cybersecurity, #AgenticAI
“The machine extends our agency, amplifies our desires, and shapes our attention; it rearranges our collective imagination, whether we notice it or not.”
— K Allado-McDowell, from an interview with Ignota Books, 2020
Modern cybersecurity strategies rely on threat intelligence to stay a step ahead of adversaries. By understanding how malicious actors operate — and why — organizations can shift from merely reacting to incidents to proactively preventing them. Commonly, threat intelligence is divided into four key categories: Strategic, Tactical, Technical, and Operational. Below, we examine these four domains, the role that Agentic AI can play in each, and how redefining “intelligence” and “agency” with human values in mind can guide the future of cybersecurity.
1. Strategic Threat Intelligence
Definition and Purpose
Strategic Threat Intelligence offers a high-level overview of an organization’s threat landscape and is often used by executive-level professionals. Rather than detailing specific attacks, it addresses broader patterns, attack motivations, and potential future risks. This knowledge informs long-term security strategies, budget decisions, and corporate policies.
Key Insights and Deliverables
Risk Assessments: Evaluating likelihood and severity of attacks.
Threat Actor Profiles: Outlining adversaries’ motives, goals, and capabilities.
Sector-Specific Trends: Forecasts for targeted industries or emerging global threats.
Strategic Documentation: Policy white papers and risk-analysis reports that shape overarching security roadmaps.
Where Agentic AI Fits…
Predictive Analytics & Scenario Planning: Agentic AI can ingest vast amounts of data — from geopolitical news to threat intelligence feeds — to predict emerging risks.
Ethical Alignment: Because decisions at this level can dramatically affect privacy, user trust, and resource allocation, ensuring AI-driven insights align with human-centric values (e.g., fairness, transparency) becomes indispensable.
2. Tactical Threat Intelligence
Definition and Purpose
Tactical Threat Intelligence focuses on tactics, techniques, and procedures (TTPs) attackers use. It highlights the methods adversaries might employ, giving security teams the information needed to anticipate and mitigate common attack vectors.
Key Insights and Deliverables
Attack Vectors and Methods: Insights on how adversaries exploit vulnerabilities.
Defense Recommendations: Patching priorities and configuration strategies.
Incident Readiness: Preparing teams via red team/blue team drills to mirror likely attacker behaviors.
Where Agentic AI Fits
Enhanced Pattern Detection: AI can identify subtle shifts in attacker playbooks earlier than manual observation.
Automated Recommendation Engines: Multi-agent systems can suggest real-time firewall rules, SIEM adjustments, or other defense tweaks to preempt attacks.
Human Oversight: AI-driven decisions here must remain transparent and reviewed for bias — ensuring that novel or underrepresented attack methods aren’t overlooked due to historical data gaps.
3. Technical Threat Intelligence
Definition and Purpose
Technical Threat Intelligence zeroes in on Indicators of Compromise (IOCs)—such as malicious IP addresses, file hashes, and phishing domains—that need continuous, timely monitoring. Because these indicators can become obsolete quickly, real-time updates and rapid sharing are critical.
Key Insights and Deliverables
IOC Discovery & Validation: Scanning for malware signatures, domain patterns, or fraudulent URLs.
Real-Time Alerting: Feeding newly discovered IOCs to security operations teams.
Continuous Updating: Rolling out updated blocklists or detection rules as new threats emerge.
Where Agentic AI Fits
24/7 Monitoring & Matching: Agents analyze vast data streams from logs, network traffic, and external feeds, correlating anomalies at machine speed.
Dynamic IOC Management: Agentic AI can instantly flag newly identified malicious domains or IPs, minimizing the window attackers have to exploit vulnerabilities.
Value-Driven Thresholds: Determining what counts as “malicious enough” to block can be tricky. AI systems must be calibrated to preserve user freedoms (e.g., minimizing false positives) while ensuring robust security.
4. Operational Threat Intelligence
Definition and Purpose
Operational Threat Intelligence provides real-time or near real-time insights into ongoing or imminent attacks. It often involves infiltrating encrypted channels or private forums where attackers plan and coordinate.
Key Insights and Deliverables
Immediate Attack Indicators: Information on timing, targets, and methods of a planned or ongoing attack.
Adversarial Motives & Tactics: Understanding what fuels the campaign and how it’s being executed.
Live Infiltration Data: Intelligence gathered from closed hacker communities, dark web markets, and encrypted chats.
Challenges & How Agentic AI Helps
Encrypted Channels & Language Barriers: AI-driven Natural Language Processing (NLP) can parse massive amounts of chat data, spotting keywords or patterns in coded discussions.
Ambiguous Terminology: Automated sentiment analysis and pattern recognition help interpret obfuscated or slang-driven language.
Ethical Considerations: Infiltrations and surveillance must be managed within legal and ethical frameworks to protect privacy and civil liberties.
Rethinking “Intelligence” and “Agency” Through a Human-Centric Lens
As AI capabilities expand, so does the urgency to clarify our definitions of intelligence and agency, ensuring these technologies align with human values rather than undermine them.
Intelligence Beyond Calculation
In cybersecurity, “intelligence” often implies data analysis and predictive prowess. However, true intelligence also involves moral reasoning and contextual wisdom. An AI might optimize for narrow objectives — blocking an IP or patching a vulnerability — yet fail to consider broader repercussions, such as privacy violations or unintended discrimination.
Redefining Agency in AI Systems
Granting AI “agency” means allowing it to act independently, in some cases without direct human oversight. This autonomy can streamline tasks like real-time threat blocking or patch management. But as soon as software decisions affect user rights or organizational transparency, ethical and accountability questions arise:
Who is responsible for an AI-driven error or oversight?
How do we ensure the AI’s decisions remain interpretable and free from harmful bias?
The Human-AI Partnership
Despite the power of automated solutions, human expertise remains crucial. Analysts bring context, creativity, and ethical judgment — elements algorithms alone cannot replicate. In a human-on-the-loop or human-in-the-loop framework, AI agents execute many tasks, but human experts oversee critical junctures, interpreting nuanced situations and ensuring alignment with organizational values and legal standards.
The Future of Threat Intelligence
As Agentic AI further permeates every dimension of threat intelligence, we can envision specialized agents conducting real-time vulnerability scanning, executing automated incident responses, and even infiltrating adversarial networks. Yet, this surge in autonomy underscores the need for holistic governance — frameworks that uphold transparency, address embedded biases, and foster public trust in AI-driven decisions. At the same time, the evolving cyber threat landscape highlights the importance of continual ethical adaptation, ensuring our security measures respect fundamental rights such as privacy and fairness, even as they defend critical infrastructure.
The four categories of threat intelligence — Strategic, Tactical, Technical, and Operational — remain the bedrock of modern cybersecurity, but their lines may blur as multi-agent AI takes on greater responsibilities. This evolution raises a deeper set of questions: How do we ensure these powerful systems genuinely serve human well-being, rather than simply amplifying existing flaws? What happens when the speed of AI-driven defenses surpasses our capacity for meaningful oversight? Rather than mere technology upgrades, the next frontier of threat intelligence demands mindful design, where our commitment to shared values guides innovation and shapes a more secure, equitable digital future.
Innovating with integrity,
@AIwithKT 🤖🧠