Embedded Analytics Security: A Comprehensive Review of Secure Embedded Business Intelligence Analytics
DOI:
https://doi.org/10.58425/ajt.v4i4.468Keywords:
Embedded analytics security, zero trust architecture, homomorphic encryption, role-based access control, data governance, AI, machine learning, business intelligence security monitoring, BIAbstract
Aim: The study aims to examine the security landscape of embedded analytics applications and to identify key threats and vulnerabilities associated with the integration of Business Intelligence and data visualization within business applications.
Methods: The research adopts an analytical review approach to assess security risks in embedded analytics environments. It evaluates common embedded BI architectures and examines existing security controls, governance mechanisms, and regulatory considerations relevant to data-driven business applications.
Results: The analysis identifies increased exposure to security risks arising from extensive use of embedded BI, including data breaches, unauthorized access, regulatory noncompliance, and unethical data use. The findings highlight gaps in security implementation and governance practices within embedded analytics deployments.
Conclusion: Embedded analytics significantly enhances real time decision making but introduces substantial security challenges. Addressing these challenges requires a structured approach that aligns technical safeguards with governance frameworks and regulatory requirements.
Recommendation: Organizations should implement robust technical controls, strengthen data governance practices, and align embedded analytics deployments with applicable regulatory frameworks to mitigate security risks and ensure secure and ethical use of Business Intelligence solutions.
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