Cyber Defense eMagazine February Edition for 2023

These authentication checkpoints can also drag down productivity if security settings aren’t streamlined
or are prone to error. Employees could find themselves erroneously locked out of systems or applications
important to their work.
A federal agency may have to rethink its security strategy to adopt zero trust, which realistically requires
a holistic approach. This kind of technology restructuring, especially when legacy systems are involved,
means protection gaps may go unnoticed — a serious risk.
Observability is key to zero trust.
Zero trust alone can’t provide all the visibility that security teams need to see across their dispersed
environments.
Agencies must wed zero trust to AI-powered cloud observability tools, which can spot gaps in protection
and ensure a seamless and secure shift to hybrid and multicloud architectures. These AI solutions
automatically monitor data and identify and remediate potentially threatening activity in real time.
Just like with zero trust, the concept of observability grew in popularity as organizations across the globe
embarked on digital transformation and began building massive cloud-native environments. They found
they needed solutions to help observe their expensive networks and make sense of data in context to
the technology stack as a whole, and they needed it in real time.
Without this kind of observability, they couldn’t fully understand the effects on users and the business.
But traditional observability tools didn’t fully satisfy their needs. Therefore, IT chiefs at federal agencies
should obtain tools that provide advanced AI-assisted observability and collect data from all system
components.
Traditional observability provides IT teams with aggregated data from three main sources: traces,
metrics, and logs. The most modern tools should offer the same information but also deliver data on fullstack,
end-to-end, code-level observability.
IT teams need AI-assisted automation
IT teams need technologies that provide continuous, automatic discovery and instrumentation and
always-on coverage with zero manual configuration. Automation is key here.
Traditional observability approaches typically required developers to manually instrument code, but this
clunky process is inefficient and even unrealistic when thousands of hosts and microservices cross global
and multicloud infrastructures.
And through code-level, precise root-cause analysis, a causation-based AI engine is also vital for
discerning actionable answers to serious problems.
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