- Below we try to give an overview of some of the most salient technical limitations of OSINT integrations we faced in project CRiTERIA:
- Compliance with Legal and Ethical Standards
- One of the biggest challenges with OSINT tools is compliance with legal and ethical standards. This includes adhering to the Terms of Service (ToS) of various platforms, respecting user privacy, and following regional laws like the General Data Protection Regulation (GDPR) in the European Union.
- Terms of Service (ToS): Social media platforms like Facebook, Instagram, and Twitter have stringent ToS that prohibit certain types of data scraping and automated data collection. Violating these ToS can lead to legal repercussions and account bans, limiting access to vital information sources.
- GDPR Compliance: In the EU, GDPR governs how personal data is collected, processed, and stored. OSINT tools need to ensure they do not infringe on the privacy rights of individuals, particularly when dealing with Personally Identifiable Information (PII). For instance, gathering data that can identify individuals, such as email addresses or physical addresses, can be legally problematic unless explicit consent is obtained. In addition, GDPR provides individuals with the right to be forgotten, which can hinder long-term data retention efforts.
- User Profiling and Tracking: While user profiling is permitted in certain contexts, GDPR imposes strict rules on how data can be used to build detailed profiles of individuals. The use of OSINT tools for profiling must ensure that they are not being used for discriminatory practices or illegal surveillance.
- Limitations in Image-Based Analysis
- Another significant technical limitation in OSINT is the use of image-based analysis, particularly in areas like facial recognition and geolocation. These technologies, while powerful, come with both accuracy and legal challenges.
- Facial Recognition: Facial recognition technology is often used in OSINT to identify individuals from publicly available images. However, the accuracy of facial recognition is not perfect and can lead to false positives, especially when used on low-quality or ambiguous images. Furthermore, in many jurisdictions, the use of facial recognition without explicit consent can be illegal. Privacy concerns are particularly pronounced in the EU, where biometric data is considered sensitive under GDPR. Many countries and regions have outright banned or stringent regulations on the use of facial recognition technologies by private entities.
- Geo-Location Limitations: Geolocation data is valuable in OSINT for tracking the movement of individuals or assets. However, obtaining precise geolocation data is often difficult due to privacy restrictions and the lack of open standards. For example, platforms like Twitter or Instagram may strip out geolocation metadata from images before making them publicly available, and accessing geolocation APIs may require explicit consent or premium access. In addition, many applications that allow users to share their location restrict the accessibility of this data to protect privacy, limiting its availability for OSINT operations.
- Lack of Openness and Standardization
- One of the technical challenges in OSINT is the lack of openness and standardization across various platforms. User-generated content (UGC) platforms like Telegram, Facebook, and others often do not provide standardized or open APIs, limiting automated data collection and analysis.
- User-Generated Content (UGC) APIs: Many platforms, including social media giants, either do not provide public APIs or limit access to certain types of data. Telegram, for instance, has restricted the use of bots in public groups, making it difficult to collect data en masse. Facebook and Instagram have also imposed significant restrictions on their Graph APIs, especially after scandals like Cambridge Analytica. These restrictions make it difficult for OSINT tools to access and analyse large datasets from these platforms.
- Platform-Specific Constraints: Each platform has its own specific constraints in terms of how data can be accessed and what types of data are available. For instance, Twitter’s API offers limited access to historical data and imposes rate limits, which can be a bottleneck for large-scale OSINT operations. Similarly, Reddit and YouTube APIs have their own limitations in terms of content retrieval and metadata access. This lack of standardization forces OSINT practitioners to develop custom solutions for each platform, increasing complexity and reducing efficiency.
- Data Silos and Fragmentation
- Data fragmentation is another critical issue in OSINT. Information is often spread across various platforms, and each of these platforms can serve as a data silo. The challenge lies in integrating and cross-referencing information across these silos.
- Data Fragmentation Across Platforms: Social media platforms, news websites, public databases, and forums each contain pieces of the puzzle, but bringing all this data together into a coherent intelligence picture is a technical challenge. The lack of interoperability between different platforms complicates the process of cross-referencing data, leading to fragmented intelligence.
- Data Access Restrictions: In many cases, even if data is publicly available, it might be difficult to access due to technical restrictions. For instance, closed platforms like WhatsApp, private Facebook groups, or forums hidden behind login walls limit the scope of OSINT.
- Reliability and Accuracy of Data
- The quality and reliability of data collected via OSINT can vary significantly, and this can pose a technical limitation when it comes to integration and analysis.
- Misinformation and Fake Data: Open sources are prone to misinformation, fake news, and manipulated content. OSINT tools must be able to filter out unreliable sources and ensure the credibility of the information they gather. This requires advanced natural language processing (NLP) and machine learning algorithms, which can be resource-intensive and complex to develop.
- Data Integrity: Another concern is the integrity of the data over time. Since OSINT relies on open sources, the data can be changed or removed at any time. For example, social media posts can be deleted, and websites can go offline, leading to gaps in the intelligence picture.
- Scalability and Performance
- Scaling OSINT operations to handle large amounts of data is a technical challenge. The need to process vast quantities of data from multiple sources in real-time can strain system resources and infrastructure.
- Real-time Processing: For OSINT to be actionable, especially in cybersecurity or threat intelligence, data often needs to be processed in real-time. This requires highly scalable infrastructure and sophisticated algorithms to handle the influx of information without delays.
- Data Storage and Retrieval: Storing large volumes of data and ensuring that it can be retrieved quickly is another technical challenge. OSINT systems need to be designed to manage both structured and unstructured data efficiently, and this requires robust database systems and optimized data retrieval mechanisms.
- Compliance with Legal and Ethical Standards
- The technical limitations of integrations in OSINT are multifaceted, ranging from legal compliance and privacy concerns to technical challenges like data fragmentation, lack of openness, and scalability. While OSINT remains a powerful tool for gathering intelligence from open sources, these limitations need to be carefully managed to ensure the accuracy, legality, and efficiency of the intelligence gathered. Developing solutions that respect legal boundaries while maximizing the potential of available data will be crucial for the future evolution of OSINT.
- GDPR Guidelines and Compliance: The official General Data Protection Regulation (GDPR) website provides detailed information on privacy rights and compliance requirements, particularly in how data is collected and processed in the EU.
- Terms of Service Restrictions: Many platforms publish their Terms of Service, which outline data collection limitations. For example, Facebook’s Terms of Service and Twitter’s Developer Agreement detail the restrictions on automated data scraping and API use.
- Facial Recognition and Privacy Concerns: Reports on facial recognition technologies and privacy, such as the Electronic Frontier Foundation (EFF), discuss the legal and ethical challenges posed by these technologies.
- OSINT Tools and Data Fragmentation: Resources like Bellingcat’s OSINT Tools and Techniques provide insights into the challenges of gathering intelligence from various platforms, including issues with data fragmentation and platform restrictions.
