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Cerbos
Cerbos Managed Authorization Service Leverages Wasm
Cerbos announced today that its Cerbos Hub managed service based on open source authorization software is now available in beta ...
Mike Vizard
|
November 6, 2023
|
authorization
,
Cerbos
,
Cerbos Hub
,
KubeCon
,
Wasm
,
WebAssembly
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The State of Incident Response and Observability
Step
1
of
7
14%
Which of the following best describes your involvement with observability or incident response in your organization?
(Required)
I actively use observability tools and respond to incidents
I select, manage, or make decisions about observability tools and workflows
I do both—use observability tools for incidents and make tooling/workflow decisions
None of the above
What are your biggest pain points in incident response today? (Select up to 4)
(Required)
Too many alerts / alert noise
Slow root cause analysis
Lack of cross-tool visibility
Manual correlation across logs, metrics, and traces
Too many people required for incidents
Lack of automation
Incomplete or missing data
Too much data
Multiplying data sources
Which of the following best describes your team’s current use of AI in observability and operations?
(Required)
Not using any AI for operations, including AI incorporated into our tools
AI assists investigations (e.g., suggests related signals or docs)
AI automatically triages incidents (e.g., classifies, routes, prioritizes)
AI takes remediation actions (e.g., executes runbooks or rollbacks)
When your team uses or has evaluated AI-assisted observability, how well does the AI include the full context of an incident—including relationships across services, infrastructure, recent changes, and team knowledge?
(Required)
AI has rich, cross-system context and surfaces relevant relationships automatically
AI has some context but frequently misses critical signals from other tools or systems
AI operates mostly on isolated data from a single tool with little cross-system awareness
AI provides generic or irrelevant suggestions that show little understanding of our environment
We have not used or evaluated AI in observability
If an AI agent could investigate incidents and identify root cause in minutes, what would you most want it to do next?
(Required)
Recommend next steps for a human to execute
Automatically remediate with human approval
Fully automate resolution without human involvement
Not ready to trust AI for any of these yet
What are the two biggest risks to your production environment today? (Select no more than 2)
(Required)
Configuration changes/drift
Untested code deployments
Underlying infrastructure issues
Database issues
Staffing constraints / key-person dependency
Skills gaps
Lack of visibility across systems
What percentage of your team’s operational time is spent on reactive incident response versus proactive prevention and improvement?
(Required)
Mostly proactive (~75% or more on PREVENTION)
Balanced (roughly 50–50)
Mostly reactive (~75% or more on RESPONSE)
Almost entirely reactive
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