Security screening metrics for information-sharing partnerships.

Area of Science:

• Business and Information Security
• Risk Management
• Natural Language Processing

Background:

• Information sharing between firms offers benefits but poses significant security risks, including privacy breaches with economic and reputational consequences.
• Evaluating security in information sharing partnerships is imperative for organizations.
• Firm privacy policies serve as a key indicator of security posture.

Purpose of the Study:

• To develop a methodology for gauging and benchmarking information security policies within the partner selection process.
• To guide risk-based investments in information sharing security.
• To provide actionable insights for managing security in inter-firm data exchange.

Main Methods:

• Collecting and interpreting firm privacy policies using natural language processing (NLP) metrics.
• Developing benchmarking metrics to evaluate policy characteristics.
• Analyzing the relationships between policy characteristics in information-sharing contexts.

Main Results:

• A demonstrated methodology applied to 500 high-revenue firms.
• Established metrics for evaluating and comparing information security policies.
• Insights into how privacy policy characteristics correlate with security in partnerships.

Conclusions:

• The developed methodology enables effective evaluation and benchmarking of information security policies.
• This approach aids organizations in making informed decisions regarding information-sharing partnerships.
• Risk managers and security professionals can leverage these insights to enhance data security and mitigate risks.