AWS
Architecture
Cloud Infrastructure
How to Design a Cloud Asset Inventory for AWS
TL;DR
Designing an effective cloud asset inventory isn't a technical problem—it's a decision-making problem. Learn the five design principles (freshness, completeness, normalization, queryability, extensibility) and maturity stages that separate systems delivering sub-five-minute response times from those taking days to answer critical security questions. The gap between containing a breach and watching it spread often comes down to inventory architecture.
Your security team discovers a critical vulnerability affecting unencrypted databases. How long does it take you to identify and remediate all affected systems?
If you're running a poorly designed cloud asset inventory—or none at all—the answer is probably days. You'll manually hunt through spreadsheets, wikis, multiple dashboards, and scattered documentation trying to piece together which systems are vulnerable.
With a well-designed, up-to-date cloud asset inventory system? Minutes. All the data is immediately accessible, queryable, and actionable.
The difference isn't just efficiency. It's the gap between containing a security breach and watching it spread across your entire infrastructure.
In this video, AWS Partner Solutions Architect Keegan Marazzi and CloudQuery Developer Advocate Joe Karlsson break down how to build cloud asset inventory systems that deliver answers in seconds, not days.
The Real Problem: Decision Velocity, Not Data Collection #
Most engineers fundamentally misunderstand cloud asset inventory systems. They approach them as data collection and engineering problems—build the pipelines, sync the resources, store the data.
However, cloud asset inventories aren't just technical problems. They're decision-making and velocity problems for the business and teams managing cloud infrastructure.
The question isn't "can we collect this data?" It's "can we make critical decisions fast enough to matter?"
After analyzing hundreds of inventory implementations across enterprises, five design principles consistently separate successful systems from failed projects.
The Five Design Principles #
1. Freshness: How Current Is Your Data? #
The reality: 68% of security incidents require immediate asset visibility. Most teams need sub-five-minute response times to address security events effectively.
The anti-pattern: Daily snapshots for security monitoring. When someone spins up a rogue instance or opens a security group to the public internet, you can't wait until tomorrow's batch run to discover it.
What freshness means:
- Real-time or near-real-time data synchronization
- Sub-five-minute detection for critical security events
- Continuous monitoring, not periodic snapshots
- Immediate visibility when resources are created, modified, or deleted
The speed of your inventory directly determines your ability to respond to threats, contain breaches, and prevent incidents from escalating.
2. Completeness: Do You See Everything? #
The reality: AWS (as of this recording) offers 240+ different services. Your inventory needs complete coverage across all of them—not just the main ones or a few S3 buckets.
What you need to capture:
- Every resource type across all AWS services
- Configuration data for each resource
- Relationships between resources
- Tags, permissions, and full metadata depth
- Coverage across all accounts and regions
Anecdotally, 90% of companies report only 40-60% visibility into their cloud environments. That means massive blind spots where security vulnerabilities, cost waste, and operational issues hide undetected.
You can't secure, optimize, or govern resources you don't know exist.
3. Normalization: Can We Join Our Data? #
This is where most cloud asset inventory systems break.
The challenge: You need schema consistency across different data sources, AWS services, and multiple accounts to get a complete view of resources across your organization.
Examples of relationships you need:
- Security groups that connect to EC2 instances
- VPC networking topology across accounts
- IAM roles and the resources they can access
- Team ownership through tags
- Environment classifications (production, staging, development)
What your data model needs:
- Universal keys (account ID, region, resource ID)
- Consistent schemas across all resource types
- Standardized field names and data types
- Relationship mappings that span services and accounts
Without normalization, you end up with data silos that can't answer cross-service questions—which defeats the entire purpose of centralized inventory.
4. Queryability: Can Teams Ask Questions About Your Cloud? #
Raw data is useless if people can't query it. Queryability determines whether your inventory becomes an operational tool or an abandoned data project.
Developer experience matters. Give your engineering and business teams the ability to ask ad-hoc questions and explore infrastructure through tools they find most useful.
What queryability requires:
- SQL databases or data warehouses for ad-hoc queries and exploration
- APIs for programmatic access to integrate with automation, scripts, and applications
- Webhook integrations for real-time updates to Slack, PagerDuty, or alerting systems
- Standardized schemas that enable integration into data pipelines and business intelligence tools
If only one person on your team knows how to extract insights from your inventory, you've created another bottleneck instead of solving visibility problems.
5. Extensibility: Can Your System Adapt to Change? #
The only constant in cloud infrastructure is change. AWS announces new services constantly—often at re
What extensibility means:
- Plug-in architecture that discovers and maps new AWS services automatically
- API-first design patterns that work with services that don't exist yet
- Data models that handle unknown resource types without breaking
- Support for new relationship patterns and service integrations
Future-proofing your inventory: New requirements emerge. Applications launch. Teams grow. Your inventory architecture must adapt without requiring complete re-architecture every time AWS releases new capabilities.
Cloud Asset Inventory Maturity Stages #
Most organizations don't leap directly to perfectly architected cloud asset inventories. They progress through predictable stages, each with distinct capabilities, limitations, and breaking points that drive evolution to the next level.
Stage 1: Ad-Hoc #
What it looks like:
- Someone asks questions about your cloud that you can't answer
- Manually clicking through AWS console dashboards
- Running ad-hoc API queries to gather information
- Excel spreadsheets shared among team members
Limitations: No automation, no consistency, massive time investment for every question.
Breaking point: Questions become too frequent, or urgency (like security incidents) requires faster answers.
If this sounds familiar, you're not alone. Everyone starts here when they begin building cloud visibility.
Stage 2: Repeatable #
What it looks like:
- Scheduled scripts (Lambda functions, cron jobs)
- Basic automation that runs nightly or weekly
- Some level of data collection consistency
Limitations:
- No real-time visibility
- Missing relationship mapping between resources
- Difficult to debug when scripts fail
- Maintenance burden as AWS APIs change
Breaking point: Scripts become too complex to maintain, or business needs require faster, more reliable data.
Stage 3: Internal APIs #
What it looks like:
- Custom-built APIs to query cloud infrastructure
- Centralized data access layer
- Some level of standardization
Limitations:
- APIs break when AWS changes underlying services
- Constant maintenance required
- Development resources diverted from business features
- Relationship management becomes complex
Breaking point: Maintenance overhead exceeds value delivered, or organization realizes they're rebuilding what managed services already provide.
Stage 4: Managed Services #
What it looks like:
- Centralized platforms with standardized queries
- Solutions like AWS Config, CloudQuery, or other cloud inventory tools
- Answer complex questions across accounts and regions
- Track changes over time with audit trails
Capabilities:
- Multi-account, multi-region visibility
- Relationship mapping across services
- Historical change tracking
- Standardized query interfaces
This is where most organizations should aim unless they have unique requirements that managed services can't address.
Stage 5: Optimized #
What it looks like:
- Real-time decision making with predictive analytics
- Inventory doesn't just tell you what happened—it predicts what will happen
- Automated remediation based on discovered issues
- Proactive identification of future problems
Capabilities:
- Trend analysis based on historical data
- Predictive modeling for capacity planning
- Automated policy enforcement
- Machine learning-powered anomaly detection
This is the gold tier for organizations that have mastered cloud asset inventory fundamentals and are investing in advanced operational intelligence.
Putting It All Together #
Good cloud asset inventory architecture starts with the five core design principles:
- Freshness - Current, real-time data that enables rapid response
- Completeness - Comprehensive coverage across all AWS services, accounts, and regions
- Normalization - Consistent schemas that enable cross-service queries and relationship mapping
- Queryability - Accessible data through SQL, APIs, and integrations
- Extensibility - Architecture that adapts to new AWS services and changing requirements
These principles apply whether you're building internal solutions or evaluating managed services. Organizations that get these fundamentals right can answer critical questions in seconds instead of days—and that speed determines whether security incidents get contained or spiral out of control.
Continue learning about cloud asset inventories with our guides on what cloud asset inventories are, maximizing value from inventories, and getting started with CloudQuery.
Video Transcript #
Keegan: Hi, everyone. Thanks for joining. My name is Keegan Marazzi, and I'm a partner solutions architect at AWS, where my team's goal is to help discover, develop, and deliver solutions with our partners. Today, I'm joined here with Joe Carlson, a senior cloud advocate developer at Cloud Query. Joe, can you introduce yourself?
Joe: Hey, my name is Joe, developer advocate, and my job is mostly just helping people get better visibility into their cloud infrastructure.
Keegan: Cool. So today we're going to be talking about some of the design considerations when it comes to building a cloud asset inventory at AWS as well as the common steps that we see companies go through in order to have successful and useful cloud asset inventories. So Joe, let's talk about our scenario.
Joe: Yeah, let's go through it. So. What I see most engineers fundamentally misunderstand their cloud asset inventory systems is that they approach it as a data collection and engineering problem. But in my humble opinion, it's more of a decision making and velocity problem for the business and people are managing the cloud. So I want you to consider a scenario to help make this a little bit more clear, but let's say your security team comes to you because they discovered a critical vulnerability affecting unencrypted databases on the cloud. If you're running a non-existent or a poorly designed cloud-ass inventory, in order to answer that question, you might have to take a couple days to go through and manually hunt across spreadsheets, wikis, multiple goodies and dashboards to find out what your systems might be affected by that. But imagine you have a well-designed, up-to-date, fresh cloud-ass inventory system. You can identify and remediate all of those vulnerabilities in a couple of minutes, because all that data is right there. And the difference here isn't just efficiency. It's a gap between containing a breach and watching it spread out across the infrastructure or become a massive issue. So how do you build a system that delivers questions you might have about your cloud in seconds and not days? I've analyzed hundreds of different inventory implementations, and we found that there are every system who is working well at enterprise level gets five things right. Let's dig into some of the considerations you make for building out your own cloud asset inventory.
Keegan: Yeah. So we're going to be talking about these five design principles you see on the right hand side of the screen. So the first principle is what we call a freshness or how current our data is. So the reality is 68% of security incidents require immediate asset visibility. And most teams need sub five minute response times. to update and address security events. Some of the anti-patterns we see are daily snapshots of security monitoring. When someone spins up a rogue instance or opens up a security group to the public internet, you can't wait until tomorrow's batch run in order to find out what's going on.
Joe: Yeah. Principle two, completeness. The question you should be asking yourself is, do we see everything in our cloud environment? AWS, as of the date of the recording, has 240 plus different services that you can access their cloud config. with, and your inventory needs to get complete coverage across all these, not just the main ones or a couple of your S3 buckets. We're talking configuration data, your relationships between those different resources, tags, permission, and full metadata depth. I'd say too, just anecdotally, I see 90% of companies I talk to probably have, they say they have 40% to 60% visibility into their cloud. That's still a lot of resources that you don't have eyes on.
Keegan: Yeah, yeah, that's a really good point, Joe. So that brings us into our third pillar, which is normalization. And the question that's asking is, can we join our data? And this is where most systems break because you need schema consistency across different data sources and across different accounts that your company manages in order to have a good rounded view of all the different resources in your organization. So it could be something as simple as security groups that connect to EC2 instances or it can be more complex depending on specific teams, tags, and ownership of different resources themselves. So this being said, your data model needs universal keys. Think account ID, think the region, whether you're just adding more data, you're gonna always make sure to have a consistent data schema across your cloud asset inventory.
Joe: Yeah, well said. So, for instance, to the fourth principle, queryability, ask yourself, can our engineering or the people who need to see this data, can they ask questions about our cloud, our cloud, right? What's going on in it? I think one of the things here that you should be thinking about is developer experience. You need to make sure that you're giving your teams like the business and engineers who are building on your AWS cloud the ability to make ad hoc queries and exploration through, you know, whatever tools they find most useful. Oftentimes we see that through SQL databases or data warehouses, but they also may need APIs for programmatic access integration. It's like webhook integration. for real-time updates or integrating into things like Slack or other alert systems, as well as standardized schemas in order to build them into data pipelines.
Keegan: Awesome. And that brings us to our final design principle, which is extensibility. So you need plug-in architecture that discover and map new AWS services automatically when AWS announces them. So whether it be a new service that comes out in re-event, your cloud asset inventory should be able to track it within days, not within months. So future proofing means API first design patterns for services that don't exist yet. Your data model must handle unknown resource types, new relationship patterns and service integrations that you haven't seen before or not using yet. So being able to future proof your cloud asset inventory is really important, especially when being agile and handling new changes within your organization.
Joe: That reminds me that like the only constant in your cloud is going to be change, right? New requirements, new apps, new things come out, things got to go, things get sunset, but being able to like improvise and that's super important. All right, cool. So we talked about the five design principles that we see are really important. If you're considering or starting to build out your cloud asset inventory, I just want to talk a little bit about common steps I see for companies as they're building them out. Most organizations don't just leap right into a golden version, perfectly built cloud asset inventory that takes some steps. It's been weird, but I've seen predictable stages, each with like distinct capabilities and limitations and breaking points where they determine they need to go on to next level. Um, so. Here's how we've broken down maturity levels and kind of see what your progression through building out a cloud S inventory might look like. So first of all, on our ladder is an ad hoc layer. This typically comes when someone starts asking you questions about the cloud that you can't answer right away. So you have to start getting that data. Maybe you're manually clicking through some of your dashboards and trying to figure out all this data. Maybe you've got access to the APIs. So you're just running ad hoc queries on that to get the data to answer those questions. And, you know, a lot of that even may be done like in Excel spreadsheets that you're kind of sharing around. That's not familiar. You're not alone. I'd say this is where everyone starts out and they're starting to build a cloud asset or they're trying to start answering questions about their AWS cloud environment.
Keegan: Yeah, so that takes us to our next step, which is being repeatable. So you have some scheduled scripts. You have some basic level automation, maybe a Lambda script that gets information about your cloud infrastructure on a nightly basis, but it's not able to really work for you every single time for different types of scenarios. So you don't have things like real-time visibility and you don't have things like relationship mapping. So any luck or any tries to debug some of your problems in these scripts are going to be really hard challenges. That takes us to the third rung on this ladder.
Joe: Yeah. So a lot of times people too, they're building their own APIs to go and query that. I think oftentimes people find that maintaining an internal API to be able to answer this question becomes annoying. APIs change, they break, they need to be maintained, the data endpoints need to be changed, new relationships need to be managed, and that becomes just kind of a pain, right? You want to be able to focus on delivering features, and this is just a thing that's helping you build a better, more scalable, safer cloud. But that brings us to building up or looking into managed services. These are more centralized platforms with standardized queries that kind of help you do that. Think like AWS Config or Cloud Query, or you're building out another solution too. but they allow you to answer complex questions about your AWS environment across accounts, across regions, and allows you to track changes over time. But you can actually go a little bit further than that.
Keegan: Yeah, and from our experience we've seen companies that have really advanced inventory systems. Asset inventory systems and we call that and categorize it as the optimized here so you have real time decision making with predictive analytics so your inventory doesn't just tell you what happened it tells you what will happen it enables you to make changes based on past trends in history of your organization in your asset utilization so. We're not talking just addressing the current problems but identifying the problems that might happen in the future. And this is really the gold tier that we have seen in our experience for companies managing their assets and their infrastructure on AWS. This kind of encapsulates the different stages and different levels of cloud asset inventories. And good inventory architecture starts with the principles that we discussed over here. So it's making sure that you have fresh current data. You have a well-rounded view of that data. So it encapsulates not only your specific infrastructure or your core components, but also the minor different configurations between your deployments. It could also, and it should include things like your test accounts and your experimentation accounts that you have engineers working on. So you have a well-rounded view. Next, we have normalization, which just makes sure that all of our data is joined and that we don't have any breaks in the styles of data. Next, we have query ability, so the ability to search that data, find and get insights on it. And lastly, we have extensibility, which, as I said before, is future-proofing the data, making sure that we're able to have room for new services that we don't account for, and being able to change quickly.
FAQ #
Q: What's the difference between AWS Config and a cloud asset inventory?
A: AWS Config focuses on compliance monitoring and configuration change tracking for AWS resources. Cloud asset inventories provide broader capabilities—multi-cloud support, custom queries, integration with external data sources, and flexible data models. Many organizations use AWS Config as one input into a broader cloud asset inventory system.
Q: How often should cloud asset inventory data be refreshed?
A: It depends on your use cases. Security and compliance often require real-time or sub-five-minute refresh rates. Cost optimization can work with daily updates. Start with hourly refreshes as a baseline, then adjust based on stakeholder needs. The 68% of security incidents requiring immediate visibility argue for real-time or near-real-time updates.
Q: Can I build a cloud asset inventory using AWS native services alone?
A: Yes, using a combination of AWS Config, Systems Manager Inventory, Resource Groups, and custom Lambda functions. However, this approach requires significant engineering effort to build queries, normalize data, and maintain as AWS releases new services. Most organizations find managed solutions more cost-effective than dedicating engineering resources to building custom inventory systems.
Q: What's the biggest design mistake organizations make?
A: Optimizing for data collection instead of queryability. Teams build elaborate pipelines that sync every possible resource, then discover nobody can actually extract insights from the data. Start with specific questions you need to answer (security compliance, cost optimization, operational visibility), then design your inventory to make those queries simple and fast.
Q: How do I handle inventory across multiple AWS accounts?
A: Use cross-account IAM roles with read-only permissions (describe and list actions). Your inventory system assumes these roles to collect data from each account, then normalizes everything into a unified schema with account ID as a key dimension. This allows queries that span all accounts while maintaining security boundaries.
Q: Should I store inventory data in a data lake or database?
A: Depends on your query patterns and scale. Databases (PostgreSQL, MySQL) work well for operational queries and relationship mapping. Data warehouses (Snowflake, BigQuery) excel at analytical workloads and massive scale. Data lakes (S3 + Athena) provide cost-effective storage with query flexibility. Many organizations use hybrid approaches—operational database for real-time queries, data warehouse for analytics and historical trends.
Q: How do I ensure my inventory captures resources across all regions?
A: Configure your data collection to iterate through all enabled AWS regions for each account. Some services are global (IAM, Route53), but most are regional (EC2, RDS, S3). Your collection logic needs to handle both global and regional resources, avoiding duplication for global services while ensuring complete regional coverage.
Q: What permissions does a cloud asset inventory need?
A: Read-only access (describe and list permissions) across all AWS services you want to inventory. Use AWS managed policies like
ReadOnlyAccess as a baseline, then refine to least-privilege access for production. Never grant write permissions to inventory systems—they should observe infrastructure, not modify it.Q: How do I measure if my cloud asset inventory design is successful?
A: Track time-to-answer for critical questions. Can you identify all unencrypted databases in under five minutes? Can you map complete network topology across accounts in seconds? Measure query performance, data freshness, coverage percentage, and—most importantly—how often teams actually use the inventory to make decisions. Unused inventories are failed inventories, regardless of technical sophistication.
