Building an agentic AI-powered threat intelligence platform

The cybersecurity industry isn’t short on threat intelligence – it’s overwhelmed by it. Every day, security teams sift through a growing flood of advisories from vendor blogs, Really Simple Syndication (RSS) feeds, premium threat providers, and opensource Indicators of Compromise (IOC) repositories. The challenge is no longer having access to intelligence, but the ability to operationalize it fast enough to reduce real risk.

When a critical advisory is published, security teams are often pushed into a slow, manual process spanning days of work reading reports, extracting indicators, and hunting across multiple tools to determine whether the organization is affected. Meanwhile, attackers are already on the move exploiting the window between intelligence publication and defender action. Fragmented feeds, inconsistent formats, and vendor-specific constraints‑ only widen this gap, making timely, actionable detection one of the hardest challenges in modern security operations.

At Adobe, our Cybersecurity Threat Research & Intelligence (CTRI) team built an AI-powered threat intelligence platform on a Lakehouse architecture to move swiftly and close the gap between intelligence and action.

In this blog, I will demonstrate how security teams can use agentic architecture and AI workflows to transform threat intelligence into timely detection and response at scale.

An agentic approach to threat intelligence

Rather than building another standalone threat intelligence platform or relying on a single vendor feed, we architected a unified, production-grade orchestration layer on a Lakehouse architecture. This layer ingests threat intelligence from any source, normalizes it into a consistent schema, and automatically hunts across enterprise telemetry in minutes.

At the core of our platform is an agentic security model: instead of relying on analyst hours to manually read advisories, extract indicators, and initiate hunts, AI agents autonomously ingest, interpret, and act on new intelligence as soon as it becomes available. Large language models (LLMs) extract IOCs from unstructured content, regardless of source format or taxonomy, which are then validated, enriched, and used to trigger coordinated hunts across the environment.

Altogether, the platform operates continuously at scale, dramatically reducing the time from intelligence publication to detection and enabling faster, more accurate response to emerging threats.

End-to-end automated workflow

Once new threat intelligence is published, the platform orchestrates a fully automated flow through the following steps:

1. Multi‑source intelligence ingestion: The platform continuously monitors dozens of threat intelligence from a wide range of sources, including commercial APIs, RSS feeds, open‑source IOC repositories, vendor blogs, and internal channels. All incoming sources are normalized into a unified data Lake schema, helping remove inconsistencies that typically slow downstream analysis.
2. AI powered IOC interpretation: When new intelligence is detected, LLMs analyze the raw content and automatically extracts relevant and structured indicators. This allows the platform to understand advisories written in different formats and taxonomies without requiring source specific parsers or manual review.
3. Orchestrated threat hunting: Instead of analysts manually translating indicators into queries across multiple tools, extracted indicators are enriched through internal parsing engines and reputation analysis systems then fed into a coordinated workflow. This workflow launches parallel hunting jobs scanning across key telemetry sources – including Endpoint Detection and Response (EDR), email security, cloud infrastructure, and authentication logs – and correlates results within a single, unified pipeline.
4. Scalable analysis across telemetry: As intelligence and data volumes grow, it scales to support timely detection without increasing operational burden on security teams. Each threat hunting‑ job processes thousands of IOCs across millions of security events using optimized SQLs, leveraging data lake partition pruning and Z-ordering to complete scans within minutes.
5. Automated notification: Detections are automatically compiled into structured reports and delivered directly to security analysts through their internal communication channels for immediate investigation and response.

By automating the end-to-end flow from intelligence ingestion through detection, this agentic approach reduces reliance on manual processes and enables security teams to operate with greater speed and consistency. The result is a more resilient threat intelligence operation, one that enables detection and response at speed without increasing operational complexity for analysts.

Designing an agentic threat intelligence architecture

While every organization’s environment is different, the core architectural patterns behind an agentic threat intelligence platform are broadly applicable. Below is a high‑level view of how security teams can approach building a similar system, without relying on a single tool, vendor, or feed:

1. Start with a unified data architecture: Design a common data schema that can represent indicators, detections, workflows, and system state in a consistent way. Normalizing intelligence from disparate sources early is critical as it allows downstream automation to operate reliably without having to account for source‑specific quirks at every step.
2. Build resilient ingestion pipelines: Ingestion pipelines should handle multiple source types and formats. At Adobe, we built connectors for each intelligence source type: RSS feed parsers with automated polling, API integrations for premium threat providers, GitHub repository monitors for open-source IOC collections, and web scrapers for security vendor blogs. Each connector handles source-specific formats, rate limits, and reliability challenges, outputting to the data lake schema.
3. Use LLMs to interpret unstructured intelligence: Use AI to enable autonomy, not just automation. Instead of relying on rigid regex parsers or source-specific logic, introduce an LLM analysis layer that can read and understand intelligence in natural language. This layer should extract structured indicators from previously unseen formats, enabling the platform to adapt as intelligence sources evolve.
4. Orchestrate detection as coordinated workflows: Detection should be treated as an orchestrated process rather than a collection of manual queries. At Adobe, we configured our workflows to coordinate parallel hunting tasks, manage dependencies, and control execution so that new intelligence consistently triggers the right downstream actions.
5. Design for scale from the start: As both intelligence and telemetry volumes grow, the system must maintain predictable performance. This requires thoughtful data layout, efficient query patterns, and orchestration logic that balances speed with resource usage. At Adobe, we implemented partition pruning (by target_source), Z-ordering on frequently queried columns, and mandatory WHERE clause filters to scan millions of rows efficiently.
6. Engineer for production reliability: Agentic systems are most effective when they understand their own boundaries. To operate reliably at scale, building in guardrails are essential – such as circuit breakers to fail fast when external intelligence sources are unavailable, retry strategies for transient failures, and asynchronous job queues to prioritize work. Comprehensive monitoring dashboards tracking pipeline health, feed reliability, error metrics, and cache performance also help teams maintain confidence in autonomous workflows and quickly detect issues.
7. Implement an automated monitoring and recheck system: Not all indicators surface immediately. Build mechanisms to revisit prior intelligence over time so the system can detect delayed signals as they emerge. By periodically re‑evaluating intelligence that initially produced no matches, security teams can catch indicators that appear later without relying on manual rechecks.

By grounding the architecture in unified data, AI‑driven interpretation, coordinated workflows, and strong guardrails, security teams can move toward an agentic model that scales with both intelligence volume and operational demands. When introducing agentic detection, running the system alongside existing processes can help teams build trust in autonomous decisions over time, easing the transition from verification to investigation.

Wrap up

Every organization faces the same challenge: an overwhelming volume of threat intelligence and too little time to act on it. By rethinking how existing data infrastructure is used, security teams can connect the tools they already have into a unified, production-grade pipeline that enables agentic, automated operations.

At Adobe, this shift has enabled us to move towards quicker threat detection and intelligence capabilities. The goal isn’t to replace analysts, but to eliminate the repetitive, mechanical work that slows them down, freeing teams to focus on mission-critical investigations and response. Agentic, AI-powered workflows offer a practical path for organizations looking to operationalize threat intelligence at scale and keep pace with a fast-moving threat landscape.