How to Prioritize AWS Trusted Advisor Findings Using a Risk and Effort Matrix

This article provides a practical framework for prioritizing AWS Trusted Advisor recommendations by scoring each finding on risk and effort, helping teams focus on high-impact, low-effort wins first.

Introduction

AWS Trusted Advisor can surface dozens or even hundreds of recommendations across an AWS environment. Teams often feel overwhelmed by the volume and struggle to determine where to start. Viewing findings is not the same as prioritizing them.

A risk and effort matrix solves this by scoring each finding on two axes: how dangerous is the issue (risk) and how hard is it to fix (effort). This produces a clear priority order that focuses your team on the highest-value work first: high-risk items that take minimal effort to resolve.

This framework works regardless of how many findings you have. Whether you're looking at 10 recommendations or 200, the scoring produces a ranked action list.

The framework

Score each Trusted Advisor finding on two dimensions:

The Priority Matrix:

	Minimal Effort	Moderate Effort	Significant Effort
High Risk	🔴 P1 — Do Now	🟠 P2 — Plan This Week	🟡 P3 — Strategic
Medium Risk	🟢 Quick Win	🟡 P3 — Schedule	⚪ P4 — Backlog
Low Risk	🟢 Batch Fix	⚪ P4 — Backlog	⚪ Accept Risk

Priority order:

• P1 (Do Now): High risk, minimal effort. Fix immediately.
• P2 (Plan): High risk, moderate effort. Schedule this sprint/week.
• P3 (Quick Win / Strategic): Either medium-risk easy fixes or high-risk architectural work. Plan accordingly.
• P4 (Backlog): Low-to-medium risk, moderate-to-high effort. Track but don't rush.
• Accept Risk: Low risk, significant effort. Document the accepted risk and move on.

Scoring risk

Rate each finding as High, Medium, or Low using these criteria:

Factor	High	Medium	Low
Pillar	Security, Fault Tolerance	Performance, Service Limits	Cost Optimization
Exposure	Public-facing or internet-accessible	Internal but cross-account	Single resource, isolated
Blast radius	Could affect multiple services or accounts	Affects one application	Affects one resource
Data sensitivity	Contains PII, credentials, or financial data	Contains operational data	No sensitive data
Compliance	Violates regulatory requirement	Best practice gap	Optimization opportunity

Scoring guidelines:

• Security findings with public exposure are always High risk.
• Fault tolerance findings on production workloads are High risk.
• Cost optimization findings are typically Low risk (money, not availability), unless they indicate abandoned resources that also pose security risk.
• Service limit findings become High risk when utilization exceeds 80% on production workloads.

Scoring effort

Rate each finding as Minimal, Moderate, or Significant:

Effort Level	Definition	Examples
Minimal	Console toggle, single CLI command, or automated fix. No change management needed. Takes < 30 minutes.	Enable CloudTrail log validation, enable S3 versioning, enable IAM Access Analyzer
Moderate	Requires scripting, testing, or coordination with another team. Takes hours to days.	Restrict security group rules (need to verify what traffic is legitimate), rotate IAM keys (need to update applications), configure KMS encryption
Significant	Architectural changes or multi-team coordination. Takes weeks.	Migrate single-AZ to multi-AZ, redesign network, implement cross-region DR

Worked example: 10 common findings

Here's how to apply the matrix to typical Trusted Advisor findings:

#	Finding	Risk	Effort	Priority
1	Security Groups: SSH (port 22) open to 0.0.0.0/0	High (security, public)	Minimal (revoke rule)	P1 — Do Now
2	IAM Access Analyzer not enabled	High (security, visibility gap)	Minimal (one-click enable)	P1 — Do Now
3	CloudTrail not logging in 5 regions	High (security, audit gap)	Minimal (enable per region)	P1 — Do Now
4	MFA not on root account	High (security, account takeover)	Minimal (enable MFA)	P1 — Do Now
5	IAM access keys not rotated (> 90 days)	High (security, credential exposure)	Moderate (update apps using keys)	P2 — Plan
6	EBS volumes without recent snapshots	Medium (fault tolerance)	Moderate (create snapshot policy)	P3 — Schedule
7	RDS Multi-AZ not enabled (production)	High (fault tolerance, production)	Significant (enable + test failover)	P3 — Strategic
8	Underutilized EC2 instances	Low (cost)	Moderate (verify then downsize)	P4 — Backlog
9	Idle load balancers	Low (cost)	Minimal (delete after confirming unused)	Quick Win
10	Lambda using deprecated runtime	Medium (security, compliance)	Significant (code migration + testing)	P4 — Backlog

Notice the pattern: items 1-4 are all P1 because they're high-risk fixes you can complete in minutes. Item 5 is high-risk but needs coordination (updating applications that use the keys), so it's P2. Items 8 and 10 can wait.

Presenting to leadership

When sharing findings with non-technical stakeholders, use this format:

Summary: 30 findings across 6 accounts

Priority	Count	Status
🔴 P1 (Fix immediately)	6 findings	All completable this week, no downtime
🟠 P2 (Plan this sprint)	4 findings	Requires coordination with app teams
🟡 P3 (Schedule)	8 findings	Mix of quick wins and larger projects
⚪ P4 (Backlog)	10 findings	Tracked, no immediate action
Accepted risk	2 findings	Documented, effort outweighs benefit

Top 3 actions for this week:

1. Enable IAM Access Analyzer across all accounts (30 min, covers 6 findings)
2. Restrict open SSH security groups (1 hour, 4 resources)
3. Enable CloudTrail log validation (15 min per trail)

This gives leadership a clear picture without overwhelming them with technical detail. They see volume, priority distribution, and concrete next steps.

Connecting to automation

Once you've identified your P1 (high-risk, minimal-effort) findings, these are ideal candidates for automated remediation. Findings that score as "Minimal effort" can often be automated entirely using EventBridge and Lambda. For a step-by-step guide on setting this up, see Automate AWS Trusted Advisor Remediation with Amazon EventBridge and Lambda.

The prioritization framework tells you WHAT to fix first. Automation tells you HOW to fix it without manual effort.

Template

Copy this table to begin your own prioritization exercise:

#	Finding	Pillar	Exposure	Blast Radius	Risk Score	Effort	Priority
1
2
3
4
5

Steps to use:

1. Export your Trusted Advisor findings (console or API)
2. For each finding, score Risk (High/Medium/Low) using the factors table above
3. Score Effort (Minimal/Moderate/Significant) using the effort definitions
4. Map to the matrix to get Priority (P1 through P4)
5. Sort by priority, then within each priority level by number of affected resources
6. Present the P1 list to your team as this week's action items

Best practices

• Re-prioritize monthly. New resources, new findings. Run this exercise on a regular cadence.
• Don't try to fix everything at once. Focus on P1 items each week. P2 items each sprint. Let P4 items accumulate until a dedicated cleanup sprint.
• Use the matrix to say no. When someone asks "why haven't we fixed X?" the matrix gives a defensible answer: "It's P4 because it's low risk and high effort. Here's what we're fixing instead."
• Track remediation over time. Count total findings per priority level each month. The P1 count should trend toward zero.
• Combine with organizational view. For multi-account environments, Trusted Advisor organizational view lets you see patterns across accounts. One security group rule open to 0.0.0.0/0 is a finding. The same rule across 15 accounts is a systemic gap.

Conclusion

Trusted Advisor gives you the findings. This framework gives you the order. Start with P1 items this week, automate the ones that recur, and use the matrix to communicate clearly with leadership about what's being fixed and why. Prioritization turns a wall of recommendations into a manageable action plan.

Related information

• AWS Trusted Advisor check reference
• Organizational view for AWS Trusted Advisor
• AWS Well-Architected Framework — Security Pillar
• Trusted Advisor Tools (GitHub)