Sat. May 18th, 2024

Content Assessment: Defining Cyber Discovery? A Definition and Framework

Information
Insight
Relevance
Objectivity
Authority

Excellent

A short assessment of the qualitative benefit of the recent post highlighting a definition and framework for cyber discovery.

Editor’s Note: While initially published in early 2021, this overview of a potential cyber discovery framework continues to grow in relevance given the acceleration of AI use across the eDiscovery ecosystem. As models permeate processes, considering an approach that combines eDiscovery planning with AI lifecycle management remains important.

Background Note: The provided overview represents a framework for cyber discovery based on artificial intelligence lifecycle stages developed by the European Union Agency for Cybersecurity, viewed through the lens of eDiscovery planning and practices from the Electronic Discovery Reference Model (EDRM).

The goal is to combine AI/ML models with eDiscovery protocols and tools to create a high-level reference model for considering cyber discovery stages and tasks. Key definitions provided include:

  • Cyber Discovery: Applying data discovery and legal discovery to explore patterns in data to uncover insight and intelligence for responding to cybersecurity challenges.
  • Data Discovery: Exploring patterns in unstructured data to uncover insight.
  • eDiscovery: Identifying, collecting and reviewing electronically stored information relevant to legal matters.

The reference model outlines stages like preparation, planning, training, tuning, discovery, and response. Each stage has specific tasks like identifying goals, selecting models, testing and training models, deploying models, and assessing model value.

The model aims to visualize an approach to cyber discovery and frame discussions on conducting cyber discovery for proactive cybersecurity assessments or reactive incident response.


Industry Backgrounder

Defining Cyber Discovery? A Definition and Framework

ComplexDiscovery Staff

Provided for your review and use is a non-comprehensive overview of definitions, depictions (graphical), and descriptions that may be helpful in considering the conduct of cyber discovery. The presented overview* represents a framework based on high-level artificial intelligence lifecycle stages as developed by the European Union Agency for Cybersecurity (ENISA) (1) modified through the lens of traditional eDiscovery planning and practices grounded within the Electronic Discovery Reference Model (EDRM) (2). The modification attempts to combine computer-centric artificial intelligence and machine learning models with data and legal discovery developed protocols and tools to provide a high-level generic reference model for considering cyber discovery stages and tasks.

In discussing the framing of cyber discovery stages and tasks within a generic reference model, it is first important to provide several definitions that may be helpful in understanding the relationships between cyber discovery, data discovery, and legal discovery.


Reference Definitions

  • Cyber Discovery: The application of a combination of data discovery and legal discovery approaches to enable the exploration of patterns, trends, and relationships within unstructured and structured data with the objective of uncovering insight and intelligence to proactively or reactively respond to cybersecurity-centric challenges. (3)
  • Data Discovery: The exploration of patterns and trends within unstructured data with the objective of uncovering insight. (4)
  • Legal Discovery (eDiscovery): The process of identifying, preserving, collecting, processing, searching, reviewing, and producing electronically stored information that may be relevant to a civil, criminal, or regulatory matter with the objective of uncovering intelligence. (5)
  • Insight: The understanding of cause and effect based on the identification of relationships and behaviors within a model, context, or scenario. (6)
  • Intelligence: The ability to acquire and apply knowledge and skills. (7)

Reference Model (Stages and Tasks)

Generic Cyber Discovery Model – May 2021

Reference Descriptions ( Stages and Tasks) 

Preparation: Initiation of the Cyber Discovery Process

  • Cyber Discovery Goals: Identifies the purpose of cyber discovery requirements. Links the purpose with the questions to be answered by the models, protocols, and tools to be used in the cyber discovery approach. Identifies model, protocol, and tool types based on the questions to be answered.
  • Data Collection and Ingestion: Identifies the input data to be collected and ingested and the corresponding context metadata. Organizes ingestion according to model and protocol requirements, importing data in a stream, batch, or multi-modal fashion.
  • Data Exploration: Identifies the attributes of data collected and ingested as assessed for use with potential models and protocols. Considers data appropriateness for answering questions related to cyber discovery goals.
  • Data Processing: Converts, integrates, and normalizes ingested data to facilitate data use as part of selected models and protocols with required applications necessary for answering questions related to cyber discovery goals.

Planning: Model and Protocol Planning

  • Model and Protocol Planning (AI + Experts): Identifies the data set dimensions based on preparation stage efforts and determines the most effective models, protocols, and tools to be selected, built, tested, trained, and tuned prior to cyber discovery.

Training: Selection, Building, Testing, and Training

  • Model and Protocol Selection and Building: Selection and building (customization) of the models, protocols, and tools most suitable for the identified cyber discovery goals.
  • Model and Protocol Testing and Training: Applies the selected models, protocols, and tools against a training set of appropriate data to validate selected cyber discovery approaches.

Tuning: Validation and Evaluation

  • Model and Protocol Validation: Applies the selected models, protocols, and tools against a validation set of appropriate data to validate selected cyber discovery approaches.
  • Model and Protocol Evaluation: Applies the selected models, protocols, and tools against a validation set of appropriate data to evaluate selected cyber discovery approaches.

Discovery: Adaptation, Deployment, and Maintenance

  • Model and Protocol Adaptation (Adjustment): Leverages pre-trained and pre-tuned models, protocols, and tools to serve as the starting point for faster and more efficient achievement of cyber discovery goals as defined by cyber discovery objective questions.
  • Model and Protocol Deployment (Execution): Takes trained models, protocols, and tools and makes them available to data scientists, data providers, and data reviewers to answer questions defined in cyber discovery objective questions.
  • Model and Protocol Maintenance (Monitoring): Monitors models, protocols, and tools and their impact on the achievement of defined cyber discovery objectives.

Response: Cyber Discovery Understanding

  • Cyber Discovery Action: Assesses the value proposition of the deployed models, protocols, and tools. Estimates (before deployment) and verifies (after deployment) the achievement of insight and intelligence objectives that can answer defined cyber discovery goal questions and drive an appropriate business, legal, or regulatory response.

This non-all-inclusive reference model may be useful for visualizing one potential approach to cyber discovery. It may also be useful for framing discussions that dive deep into the conduct of specific cyber discovery actions ranging from proactive cybersecurity assessments to reactive post-data breach discovery and review efforts in support of incident responses.

References

(1) European Union Agency for Cybersecurity, 2020. Artificial Intelligence Cybersecurity Challenges. [online] European Union Agency for Cybersecurity. Available at: https://digital-strategy.ec.europa.eu/en/library/report-artificial-intelligence-cybersecurity-challenges [Accessed 2 May 2021].

(2) EDRM | Empowering the Global Leaders of eDiscovery. 2021. EDRM. [online] Available at: https://edrm.net/ [Accessed 2 May 2021].

(3) Robinson, R., 2021. Considering Cyber Discovery? A Strategic Framework. [online] ComplexDiscovery. Available at: https://complexdiscovery.com/ [Accessed 2 May 2021].

(4) All, A., 2014. Data Discovery Is Changing Business Intelligence. [online] Enterprise Apps Today. Available at: http://www.enterpriseappstoday.com/business-intelligence/data-discovery-is-changing-business-intelligence.html [Accessed 2 May 2021].

(5) Grossman, M. and Cormack, G., 2013. The Grossman-Cormack Glossary of Technology-Assisted Review. Federal Courts Law Review, [online] 7(1). Available at: https://www.fclr.org/fclr/articles/html/2010/grossman.pdf [Accessed 2 May 2021].

(6) Wikipedia. 2021. Insight. [online] Available at: https://en.wikipedia.org/wiki/Insight [Accessed 2 May 2021].

(7) In: Lexico (Oxford). 2021. Intelligence. [online] Available at: https://www.lexico.com/definition/intelligence [Accessed 2 May 2021].

*Modified and shared with permission under Creative Commons – Attribution 4.0 International (CC BY 4.0) – license.

Assisted by GAI and LLM Technologies

Additional Reading

Source: ComplexDiscovery

 

Have a Request?

If you have information or offering requests that you would like to ask us about, please let us know, and we will make our response to you a priority.

ComplexDiscovery OÜ is a highly recognized digital publication focused on providing detailed insights into the fields of cybersecurity, information governance, and eDiscovery. Based in Estonia, a hub for digital innovation, ComplexDiscovery OÜ upholds rigorous standards in journalistic integrity, delivering nuanced analyses of global trends, technology advancements, and the eDiscovery sector. The publication expertly connects intricate legal technology issues with the broader narrative of international business and current events, offering its readership invaluable insights for informed decision-making.

For the latest in law, technology, and business, visit ComplexDiscovery.com.

 

Generative Artificial Intelligence and Large Language Model Use

ComplexDiscovery OÜ recognizes the value of GAI and LLM tools in streamlining content creation processes and enhancing the overall quality of its research, writing, and editing efforts. To this end, ComplexDiscovery OÜ regularly employs GAI tools, including ChatGPT, Claude, Midjourney, and DALL-E, to assist, augment, and accelerate the development and publication of both new and revised content in posts and pages published (initiated in late 2022).

ComplexDiscovery also provides a ChatGPT-powered AI article assistant for its users. This feature leverages LLM capabilities to generate relevant and valuable insights related to specific page and post content published on ComplexDiscovery.com. By offering this AI-driven service, ComplexDiscovery OÜ aims to create a more interactive and engaging experience for its users, while highlighting the importance of responsible and ethical use of GAI and LLM technologies.