The biggest bottleneck in threat intelligence today is not a lack of data. It is the bandwidth required to consume it.
Security teams are currently drowning in RSS feeds, vendor PDFs, and disjointed indicators of compromise (IOCs). Analysts spend hours filtering out noise just to find the one relevant signal that impacts their specific environment. The industry has become very efficient at collecting data, but we are still struggling with the synthesis required to make that data actionable.
We built ThreatLandscape.ai to change this consumption model. We moved away from the concept of a search engine—which simply retrieves more documents for you to read—and built an analytical partner that helps you interrogate the data directly.
The Problem: The "Stitching" Phase
In a traditional workflow, answering a simple question about a threat actor involves:
- Searching across multiple disjointed reports.
- Skimming long PDFs for relevant TTPs.
- Manually extracting IOCs and cross-referencing them with your SIEM.
- "Stitching" these facts together to form a hypothesis.
This process is slow, manual, and prone to burnout. By the time the intel is correlated, the adversary may have already pivoted.
The Solution: Conversational Analysis
ThreatLandscape.ai is an interactive threat intelligence copilot. It allows you to skip the manual correlation phase and work directly with an AI assistant that understands threat intel workflows.
We recently tested this with a specific query: "What active or emerging threats are targeting European banking organizations currently?"
Behind the scenes, the Copilot accessed tens of thousands of threat intelligence facts, updated hourly. Because we set the persona to Threat Intelligence Analyst, the system didn't just summarize generic news. It highlighted:
- Relevant threat actors currently active in the region.
- Specific TTPs being utilized.
- Behaviors that matter specifically for financial institutions.
The result was concise, contextual, and immediately usable.
Adapting to Your Role
One of the core failures of general-purpose AI is that it treats every user the same. In security, a CISO needs a very different answer than a Detection Engineer, even if they are asking about the same threat.
ThreatLandscape.ai adapts its output based on your role:
- Threat Intel Analysts: Ask for TTP extraction, ATT&CK mapping, and adversary summaries.
- Incident Responders: Investigate and enrich IOCs through guided analysis during a live incident.
- Detection Engineers: Explore threat behavior to generate detection logic on demand.
- Red / Purple Teams: Interactively model adversary activity to build realistic attack scenarios.
- CISO / Leadership: Query the same intel through a risk-focused lens without technical oversimplification.
Why Context Matters
There is a distinct difference between summarizing a report and analyzing it. General-purpose LLMs are excellent at summarizing text you paste into them. However, in threat intelligence, the context you need is rarely in the document you are currently reading.
Real insight comes from historical data, unrelated campaigns from six months ago, or infrastructure overlaps that a human analyst might miss due to volume. Our platform connects these dots, checking your input against a massive, continuously updated knowledge base.
Try It in Your Workflow
We know that seeing a demo is not the same as getting hands-on with the tool. To trust an AI assistant, you need to validate it against your own intelligence requirements.
We have launched an Evaluation Access tier for this exact purpose.
- Cost: $9 (One-time, non-renewable).
- Duration: 7 Days.
- Capacity: 50 Queries.
This provides enough runway to test the system against your actual workflows. Throw your hardest questions at it—whether you are hunting for specific TTPs or enriching a set of indicators—and see if it reduces your time-to-answer.
If you decide to upgrade to a monthly plan, we credit the evaluation cost back to your first month.
Stop reading reports and start interrogating them.