SILVA, R. S.; http://lattes.cnpq.br/3141388403876772; SILVA, Raiff dos Santos
Résumé:
The current security landscape demands efficient processes that correspond to the advancement
of techniques employed in cyber attacks. From this perspective, process automation is
seen as an alternative to meet the security needs required in information systems, benefiting
manual activities with optimizations and providing scalability. Observing security standards
or often legislation that require shaping a security posture for maintaining data privacy or
specific areas of operation, compliance is present as a means of proving the good practices
implemented.
Compliance, often obtained through audit processes by certifying bodies, can also be
achieved through the use of security frameworks. Thus, CIS Controls V8 and the NIST Cybersecurity
Framework have widely used security controls. In this study, we define how to
use these security frameworks as a basis for security recommendations to provide compliance
for vulnerability management processes. We use CIS Controls V8 to extract relevant
keywords with the use of machine learning algorithms that enable the discovery of security
tools, thus supporting our technical cybersecurity implementations. We operationalize compliance
by using methods to measure compliance using vulnerability verification metrics and
security recommendation coverage.
Finally, we aggregate all our learning from the operationalization of compliance to propose
a continuous compliance model. This model considers process automation for the
execution of vulnerability verification tools to collect security evidence about the system
architecture. It also includes the analysis of vulnerabilities harmful to security using our
verification metric that defines the exploitability and impact related to the assets verified in
our processes.