Context
As cybersecurity threats have grown more sophisticated in the automotive industry, the need for a reliable and collaborative intelligence ecosystem has become increasingly obvious. Traditional IT threat-sharing models didn’t address the unique challenges posed by the complexity of automotive systems. That is why John Heldreth, then working at Porsche, launched what would become the Automotive Security Research Group (ASRG) in 2016: “We realized that the community would play a very important role in the success of cybersecurity for automotive. It doesn’t matter which company you work for: you’re either part of the solution or you’re on the other side.” ASRG quickly evolved into a global initiative with over 20,000 members, supported by a lean core team and hundreds of active volunteers contributing to projects worldwide.
Challenges
Limited interoperability and external sharing
While ASRG was already using a CTI platform, it lacked the flexibility and openness needed to organize, contextualize, and share information efficiently. The system was designed for internal use only, which made it difficult to build meaningful relationships between objects or expose threat intelligence externally. “We create our own threat intelligence based on industry input, but we lacked a place to structure it, channel it, and make it usable for others,” recalls John Heldreth. To enable collaboration, the team had to manually extract data via custom APIs, reformat it, and rehost it in external tools. “In general, companies want to keep threat information secret, but we needed the opposite,” John adds. This workaround created friction, increased the risk of errors, and made real-time information exchange nearly impossible.
High infrastructure & maintenance costs
Maintaining two parallel systems – one for internal CTI management and one for external dashboards – resulted in significant overheads. ASRG had to run large servers and shoulder the cost of maintaining redundant infrastructure. This model was not sustainable for a non-profit organization operating on limited resources.
Time-consuming workflows
Data enrichment and dashboard generation were largely manual processes, that required extensive time and effort to set up and maintain. “It used to take over 40 hours of work,” explains John Heldreth. This slowed down ASRG’s ability to effectively disseminate information, limiting the platform’s responsiveness to evolving threats and community needs.
Lack of traceability in enrichment processes
When ASRG began automating enrichment using AI models, it faced a new challenge: tracking the origin and logic behind each piece of generated data. John says: “We needed to ensure the quality of the data. Every step of the enrichment process had to be traceable (who created what, and why) so that we could trust the data.” To build transparency and for audit purposes, ASRG needed a platform that could preserve this end-to-end traceability at every stage of enrichment.
Why did ASRG Choose Filigran’s OpenCTI
Cost-effective & open source by design
As a non-profit organization with limited resources, ASRG needed a platform that could scale without licensing constraints or vendor lock-in. While their previous CTI solution was used at no cost, it lacked the flexibility and openness required for automation, enrichment, and community-driven sharing. OpenCTI’s open-source nature made it possible to get started without financial barriers. “It was mostly cost-driven at the beginning,” John Heldreth explains. This made Filigran’s OpenCTI a great option to scale securely and independently.
Structured, interoperable, and based on open standards
OpenCTI’s compliance with the STIX 2.1 standard provided ASRG with a clear, interoperable framework for structuring threat intelligence. “If someone understands the STIX schema, then they understand OpenCTI,” explains John, “That makes it easier for us to structure the data and build meaningful relationships between objects.” While STIX is not perfect, it provides a critical foundation for sharing knowledge across use cases.
API-first architecture enabling automation & enrichment
ASRG needed the freedom to extract, enrich, and reintegrate data according to its own workflows, especially to support its AI-driven enrichment pipeline. This process includes 32 sequential steps, where raw data is extracted from OpenCTI, analyzed, categorized, linked to relevant components, and continuously updated before being sent back to the platform. This end-to-end flow enables a continuous, bidirectional data loop. “We have to do everything with minimal resources, so we use as much automation and artificial intelligence as possible,” says John Heldreth. “Being able to orchestrate that process end to end without being limited by the platform was essential.” OpenCTI’s robust API and Python libraries gave the team full control over how data flows in and out of the system.