When the Console Lies

A hidden AWS SecurityHub configuration policy, invisible in the console but active at the API level, was silently draining budget. The AWS Management Console showed SecurityHub as disabled. CLI commands returned confusing errors. Manual investigation led nowhere. This is the reality of modern cloud infrastructure: what you see isn't always what you get.

Enter Amazon Q - AWS's agentic AI assistant. In just 15 minutes, Q identified the root cause through systematic API introspection, multi-step reasoning, and tool execution. It discovered a configuration policy attached at the organization root level that wasn't visible through standard interfaces.

This isn't about (generative) AI replacing engineers. It's about AI enhancing our capabilities to manage increasingly complex cloud platforms where console UIs can't always reveal the true state of resources.

The SecurityHub Mystery: When the Console Shows One Thing, APIs Show Another

Modern cloud platforms have become so complex that console UIs can't always reveal the true state of resources. This leads to hours of manual investigation that requires deep service knowledge. In this case, an engineer encountered a critical situation:

The Symptoms

• SecurityHub appeared disabled in the AWS Console's delegated admin account
• Unexpected charges continued to accrue for SecurityHub despite the disabled status
• CLI commands failed with confusing errors about "central configuration"
• No visible configuration policies in the delegated admin console
• Traditional troubleshooting approaches (documentation, support forums) provided no clarity

The Pain Points

• Console Limitations: The AWS Console provides an abstracted view that doesn't always reflect the underlying API state. Configuration policies set at the organization root level may not appear in delegated admin views, creating a blind spot.
• Deep Service Knowledge Required: Diagnosing API-level configuration issues requires deeper knowledge of service with specific APIs, parameters, and organizational hierarchy interactions — knowledge that may taker longer to build and is difficult to maintain across hundreds of services.
• Time-Consuming Investigation: Manual API investigation can take hours or days. Engineers must methodically explore APIs, read specific documentation, validate hypotheses, and iterate — all while the issue continues to impact production systems or incur costs.
• Hidden Costs: Configuration drift and hidden policies can lead to unexpected charges. In this case, SecurityHub continued to run (and bill) despite appearing disabled, demonstrating how API-level discrepancies directly impact FinOps.

The Traditional Approach Would Involve:

1. Searching AWS documentation for "SecurityHub central configuration" error messages
2. Reading through SecurityHub API reference to identify relevant API calls
3. Manually executing CLI commands to inspect organization configuration
4. Testing hypotheses about configuration policy associations
5. Potentially opening an AWS support ticket and waiting for response
6. Estimated time: Hours to days

Amazon Q's Diagnostic Approach: Agentic Reasoning in Action

For identifying this particular SecurityHub mystery, Amazon Q didn't simply search documentation or suggest generic troubleshooting steps. Instead, it showcased agentic reasoning — forming hypotheses, executing API calls, checking results, and moving toward a solution.

Step 1: Hypothesis Formation

Q's Initial Reasoning: "The console shows SecurityHub as disabled, but charges are accruing. This suggests a disconnect between the console view and the actual API state. The error message mentions 'central configuration,' which indicates organization-level settings may be involved."

Step 2: API Introspection - Organization Configuration

Q's first action was to check the organization-level configuration status using the SecurityHub API:

aws securityhub describe-organization-configuration \
    --region us-east-1

API Response:
{
    "AutoEnable": false,
    "MemberAccountLimitReached": false,
    "OrganizationConfiguration": {
        "ConfigurationType": "CENTRAL",
        "Status": "ENABLED"
    }
}

Central configuration is ENABLED at the organization level, confirming the hypothesis that organization-level settings are overriding the console view.

Step 3: Policy Discovery

Knowing that central configuration was enabled, Q's next logical step was to identify configuration policies:

aws securityhub list-configuration-policies \
    --region us-east-1

API Response:

{
    "ConfigurationPolicySummaries": [
        {
            "Arn": "arn:aws:securityhub:us-east-1:123456789012:configuration-policy/a1b2c3d4-5678-90ab-cdef-EXAMPLE11111",
            "Id": "a1b2c3d4-5678-90ab-cdef-EXAMPLE11111",
            "Name": "OrgSecurityHubPolicy",
            "Description": "Organization-wide SecurityHub configuration"
        }
    ]
}

Outcome: A configuration policy exists but wasn't visible in the delegated admin console. This policy is the root cause of the mysterious behavior.

Step 4: Policy Association Detection

Q then verified where this policy was associated within the organization hierarchy:

aws securityhub get-configuration-policy-association \
    --target '{"Type":"ROOT","Id":"r-abcd"}' \
    --region us-east-1

API Response:
{
    "ConfigurationPolicyId": "a1b2c3d4-5678-90ab-cdef-EXAMPLE11111",
    "TargetId": "r-abcd",
    "TargetType": "ROOT",
    "AssociationType": "APPLIED",
    "UpdatedAt": "2025-10-15T14:32:00.000Z"
}

Root Cause Identified: The configuration policy is associated at the organization root level. This is why it wasn't visible in the delegated admin console view, which typically shows account or organizational unit level configurations. The policy was enforcing SecurityHub at the root, overriding any account-level disable operations.

What Makes This "Agentic" vs Traditional AI

The term "agentic AI" is becoming common marketing keyword nowadays, but what does it actually mean in practice? This SecurityHub case study provides a clear example of the difference between traditional AI assistants and true agentic systems.

Best Practices & Recommendations

Based on this SecurityHub case study and broader experience with agentic AI in cloud operations, here are actionable recommendations for Amazon Q's effectiveness in your environment.

Do's:

Start with Detailed Context: Provide comprehensive problem descriptions including error messages, affected services, recent changes, and what you've already tried. The more context Q has, the more targeted its investigation.

"SecurityHub shows disabled in console but Cost Explorer shows $500 charges last month. 
CLI error: 'Cannot disable due to central configuration.' 
I'm in the delegated admin account. 
No policies visible in console under Settings > Configuration Policies."

Use Q for API-Level Investigations": Amazon Q excels when console views are inconsistent with API state. Leverage its ability to execute API calls directly rather than relying on manual CLI troubleshooting. This is particularly valuable for organization-level configurations that may not be visible in account-level console views.

Test in Non-Production First: Before applying Q's recommendations to production resources, validate the approach in development or staging environments. This builds confidence and helps you understand the solution's mechanics without production risk.

Treat Q as a Learning Tool: Don't just execute Q's commands blindly—ask it to explain why each step is necessary. This transforms troubleshooting sessions into learning opportunities that build your AWS expertise over time.

Provide Feedback on Errors: When Q makes a mistake, share the error message and ask for correction. This iterative feedback loop improves the AI's responses within your conversation and helps it learn from context.

Document Q-Driven Solutions: Create runbook entries for problems Q solves, including the diagnostic path and resolution steps. This builds institutional knowledge and allows the team to resolve similar issues faster in the future.

Don'ts: Common Mistakes to Avoid

Don't Execute Commands Blindly: Always review generated commands for correctness. Verify resource identifiers, region parameters, and understand the impact before execution. AI can make mistakes—human validation is essential.

Don't Expect 100% Accuracy: Amazon Q is sophisticated but not infallible. Cross-reference critical recommendations with official AWS documentation. Maintain healthy skepticism and validate solutions.

Don't Skip Security Review: Granting AI tools account access requires careful permission management. Follow least-privilege principles and regularly audit Q's IAM roles. Never grant write permissions unless absolutely necessary and in non-production only.

Don't Ignore Learning Opportunities: Using Q as a pure automation tool misses its educational value. Take time to understand why solutions work. Ask Q to explain the underlying AWS concepts, service relationships, and best practices.

Don't Use Q for Emergency Incidents (Initially): Build confidence with Q on non-critical issues first. During production outages, rely on proven processes. Integrate Q into incident response only after the team is comfortable with its capabilities and limitations.

Don't Share Proprietary Business Logic: While AWS services are fair game for Q, avoid sharing proprietary algorithms, business rules, or sensitive application logic. Stick to infrastructure and configuration troubleshooting.

Agentic AI like Amazon Q represents a fundamental shift in cloud operations, moving from AI as a sophisticated search engine to AI as an autonomous problem solver.

• Agentic AI Is Genuinely Different: Amazon Q doesn't just suggest solutions—it investigates problems through multi-step API calls, causal reasoning, and iterative refinement. This is qualitatively different from traditional AI assistants.
• Console Views Can Mislead: Modern AWS has grown so complex that console UIs can't always reflect the true API state.
• Massive Time Savings Are Real: The improvement in resolution time isn't theoretical—it's demonstrated. This translates directly to reduced engineering costs, faster incident response, and better resource utilization.
• Security Can Be Maintained: Granting Q read-only access provides powerful diagnostic capabilities without security risk.
• Human Judgment Remains Critical: AI accelerates troubleshooting but doesn't replace engineering judgment. Always validate recommendations, understand the why behind solutions, and maintain change control processes.
• The Technology Is Mature Enough: If you dismissed Amazon Q based on early experiences, it's time to revisit.

As AI agents become more sophisticated and gain access to more AWS APIs and organizational context, they will increasingly handle tier-1 and tier-2 operational tasks autonomously. This shift allows engineers to focus on:

• Architecture and design decisions that require strategic thinking and business context
• Cost optimization initiatives that balance technical and financial considerations
• Innovation and experimentation with new AWS services and patterns
• Complex problem solving that requires domain expertise and creative thinking