MISSM

Abstract— Database as a Service (DBaaS) is one of the
key cloud computing services that is well-known as a
type of Application-as-a-Service which gives users
access to a database without downloading and installing
software or performance configuration but maintains
the customers database. DBaaS assumes the
responsibility of traditional database administration
software in which data owners and clients can build,
update, delete, and have access to database services
without installing physical hardware and it is also
economically feasible for users. Despite the advantages
that DBaaS has, it suffers from many challenges, which
need considerable security. For example, providers are
liable to infringe consumer confidence with the risk that
data security and privacy may be impeded. Additional
overheads of remote network access, data security
infrastructure, and user interface design for such a
service are among the key issues that DBaaS faces. So,
the migration of databases without impacting the
consistency of the solutions is still in its infancy. This
research paper identified security and privacy issues in
DBaaS and offered adequate solutions to mitigate it.
The identified security and privacy issues are discussed
with their consequences and the security functions are
taken into consideration to demonstrate the impacts on
security purpose. With that, the provision of security
controls by three vendors (Amazon, Microsoft Azure
and Oracles) and together with related security controls
and best practices from ISO
27001/2013, CSA/CCMv3.0.1 and NIST 800-53 R5 are
mapped with the identified issues to aid the creation of
security controls to mitigate the risks.

Healthcare organizations are increasingly relying on artificial intelligence (AI) to enhance security measures in response to the growing threat of cyber-attacks and the importance of safeguarding sensitive patient information. This study delves into the impact of AI on healthcare security, with a focus on five crucial areas: anomaly detection, predictive analytics, access control, threat intelligence, and incident response. The results demonstrate that AI can effectively pinpoint unusual patterns or behaviors that may indicate a security breach through anomaly detection. By analyzing historical security breaches and predicting future attacks, AI offers a proactive approach through predictive analytics. Furthermore, AI can streamline user access to patient data by automatically managing permissions based on established rules, thereby enhancing access control. By monitoring and analyzing threat intelligence feeds, AI enables healthcare organizations to stay abreast of the latest security threats and vulnerabilities, bolstering threat intelligence efforts. In incident response, AI proves invaluable by issuing real-time alerts, automating responses to specific incidents, and providing valuable insights into the root causes of security breaches. Ultimately, this study underscores the critical role that AI can play in fortifying healthcare security and safeguarding patient data against cyber threats.