- The next generation of AWS Resilience Hub introduces generative AI for automated failure mode analysis, identifying risks that human review might miss.
- Modular resilience policies allow teams to compose custom requirements based on specific application needs, moving away from one-size-fits-all templates.
- Automated dependency discovery and topology mapping provide a clear, dynamic view of resource interconnections, revealing hidden risks.
- Integration with AWS Organizations enables centralized resilience evaluation and reporting across an entire enterprise, simplifying compliance and governance.
- The new features aim to provide SREs and development teams with a structured, repeatable process for setting resilience goals, measuring progress, and proving compliance at scale.
- This update represents a significant shift towards AI-infused operations and platform engineering principles within AWS.
The Resilience Challenge at Scale
Managing hundreds of applications across a large organization presents significant resilience challenges. Teams often set varying availability goals, use different monitoring tools, and track progress inconsistently. This leads to a lack of clear visibility into overall application resilience and makes it difficult to prove compliance to auditors or answer executive questions about system readiness.
The original AWS Resilience Hub aimed to address these issues by assessing individual applications. However, it had limitations in enterprise-wide scalability, consistent policy definition, and manual identification of failure modes.
At re:Invent 2025, AWS introduced the next generation of AWS Resilience Hub. This enhanced version incorporates generative AI, modular policies, automatic dependency discovery, and integration with AWS Organizations to provide a structured and repeatable approach to setting resilience goals, measuring progress, and ensuring compliance across an entire application portfolio.
What’s New in the Next Generation of Resilience Hub
The latest iteration of Resilience Hub represents a fundamental shift in resilience management, moving towards a unified framework that links resilience directly to business outcomes. It introduces five key capabilities:
- A new application model connecting critical user paths to business objectives.
- Automated dependency discovery for building a live resource topology.
- Generative AI-powered failure mode analysis to identify overlooked weaknesses.
- Modular resilience policies allowing for custom requirement composition.
- Integration with AWS Organizations for centralized evaluation and reporting.
This integrated approach guides Site Reliability Engineers (SREs) and development teams through the entire resilience lifecycle. It begins with defining applications and critical user journeys, followed by automatic resource discovery and topology mapping. Resilience policies are then applied, and the AI engine analyzes the architecture to identify potential failure modes. The final output is a comprehensive report detailing compliance status and recommendations for improvement, a significant upgrade from manual documentation and analysis.
Modular Resilience Policies: Build What You Need
A key practical advancement is the ability to create resilience policies using modular building blocks. This allows teams to select and combine specific requirements tailored to their application’s needs, moving away from rigid, one-size-fits-all templates.
Available policy modules include:
- Service Level Objective (SLO): Define specific uptime targets, such as 99.9% or 99.99%.
- Multi-AZ resilience: Ensure the application can withstand the failure of a single Availability Zone.
- Multi-Region disaster recovery: Establish requirements for failing over to a secondary AWS Region.
- Data recovery: Specify Recovery Point Objective (RPO) and Recovery Time Objective (RTO) for data.
This modular approach enables the creation of precise policies. For instance, a less critical web application might only require multi-AZ resilience with a 99.9% SLO, while a core database could necessitate multi-Region DR with a 15-minute RPO and a 2-hour RTO. Policies are versioned and stored, facilitating tracking and enforcement across multiple applications.
Generative AI Failure Mode Analysis: Catching What Humans Miss
The generative AI-powered failure mode analysis is a standout feature, offering a significant improvement over traditional Failure Mode and Effects Analysis (FMEA).
Traditional FMEA involves manual brainstorming and documentation, which is time-consuming, prone to human error, and often incomplete for large, complex systems. It can take weeks or months to conduct a thorough FMEA, and subtle failure modes may still be missed.
The generative AI in Resilience Hub automates this process. After defining the application model and attaching a resilience policy, the AI scans the architecture and configurations. It checks for compliance with policy requirements, such as verifying multi-AZ deployments or comparing backup schedules against RPO targets. If a discrepancy is found, it’s flagged as a potential failure mode.
Beyond basic configuration checks, the AI can reason about complex scenarios like cascading failures and cross-account dependencies. By leveraging AWS’s operational experience and common cloud failure patterns, it provides a richer analysis than simple rule-based checks. This allows SREs to obtain comprehensive failure mode analyses in minutes, enabling iterative improvements and catching regressions before they impact production.
Automated Dependency Discovery and Topology Mapping
Understanding resource interdependencies is crucial for resilience but often challenging in complex cloud environments. Resources are frequently distributed across multiple accounts, VPCs, and regions, making manual mapping a daunting task.
Resilience Hub now automatically discovers AWS resources and constructs a visual topology, mapping service relationships based on configuration and usage. This includes tracing paths like a Lambda function writing to an SQS queue consumed by an EC2 instance. The generated graph reveals all dependencies, including cross-account connections when AWS Organizations is used.
This automated discovery significantly reduces manual documentation effort and uncovers hidden dependencies that could lead to cascading failures. The topology map is dynamic, updating automatically as the architecture evolves, ensuring it remains current for ongoing operations and resilience assessments.
Enterprise-Wide Reporting with AWS Organizations
For large enterprises, centralized governance and compliance are paramount. The integration with AWS Organizations provides a single pane of glass for evaluating resilience across all accounts.
Organizations can define baseline resilience policies at the root level and apply them universally or customize them for specific organizational units. Resilience Hub then aggregates results, highlighting compliant applications and identifying those with gaps. This offers leadership a clear, auditable view of the organization’s overall resilience posture.
For example, a financial services company can mandate a multi-Region DR policy for all production workloads handling customer data. Resilience Hub automatically verifies compliance across hundreds of accounts, flagging non-compliant applications and providing remediation recommendations. This capability is invaluable for audits and regulatory reviews, replacing manual data collection with automated, centralized reporting.
How It Fits into the AWS re:Invent 2025 Announcements
The enhanced Resilience Hub is part of a broader AWS strategy focused on AI-infused operations, alongside announcements like Frontier agents and new Amazon Nova models. It exemplifies the use of generative AI to automate traditionally manual and error-prone tasks in operational resilience.
This update aligns with the trend toward platform engineering, where infrastructure teams provide self-service tools for developers. The integration with AWS Organizations and modular policies supports centralized governance with decentralized execution, allowing platform teams to set guardrails while application teams build resilience into their services.
AWS is positioning itself as a leader in operational resilience for cloud-native environments. The combination of generative AI analysis, modular policies, and enterprise-wide reporting offered by Resilience Hub addresses a key customer need for a scalable and consistent approach to managing resilience across extensive application portfolios.
Getting Started with Your Resilience Journey
To begin using the new Resilience Hub, the initial step involves defining your application model within the AWS Management Console. Navigate to Resilience Hub, create a new application, and optionally link it to an AWS Organization. The tool will then commence resource discovery in the specified accounts.
Next, define the critical user journeys for your application-the key business flows essential for delivering value. Map these journeys to the underlying services and resources.
Subsequently, create or select a resilience policy by choosing applicable modules (e.g., multi-AZ, multi-Region DR) and setting targets like SLOs, RPOs, and RTOs. Attach this policy to the application.
Resilience Hub will then perform the generative AI analysis against the policy, generating a list of potential failure modes with severity ratings and remediation suggestions. Review these findings, address the issues, and re-run the assessment until a satisfactory report is achieved.
Finally, configure automated checks to re-evaluate the application upon detecting changes, ensuring resilience is maintained over time. Compliance reports can also be generated for auditors and management.
While designed for existing AWS users, the tool is accessible even without deep resilience expertise, thanks to its guided workflow and AI analysis. It empowers SREs with advanced capabilities and provides business leaders with the transparency and accountability needed for confident operations.
Frequently Asked Questions
What is the main problem AWS Resilience Hub aims to solve?
AWS Resilience Hub aims to solve the challenge of managing application resilience at scale within large organizations. It addresses issues like inconsistent availability goals, scattered monitoring tools, and difficulty in proving compliance or understanding system dependencies across hundreds of applications.
How does generative AI improve failure mode analysis in Resilience Hub?
Generative AI automates the traditionally manual and time-consuming FMEA process. It scans application architectures against resilience policies to identify potential failure modes, including complex scenarios and cross-account dependencies, often catching issues that human analysis might overlook.
What are modular resilience policies?
Modular resilience policies allow users to build custom resilience requirements by combining pre-defined building blocks, such as SLOs, multi-AZ resilience, multi-Region DR, and data recovery objectives. This enables tailored policies that fit specific application needs rather than using rigid templates.
How does AWS Organizations integration benefit enterprise users?
Integration with AWS Organizations provides a centralized dashboard for evaluating resilience across all accounts within an organization. It allows for setting organization-wide policies, aggregating compliance status, and generating auditable reports, simplifying governance and compliance efforts.
What is automated dependency discovery in Resilience Hub?
Automated dependency discovery scans AWS resources to map their relationships and dependencies, creating a visual topology of the application's architecture. This helps uncover hidden connections and potential points of failure that might not be apparent through manual documentation.
Is the new Resilience Hub suitable for users who are not resilience experts?
Yes, the new Resilience Hub is designed with a guided workflow and AI-powered analysis to handle complexity. It aims to make resilience management more accessible, allowing SREs and development teams to benefit even without deep expertise in resilience engineering.
How does Resilience Hub help in proving compliance?
Resilience Hub provides clear, auditable reports that demonstrate an application's or an entire portfolio's compliance with defined resilience policies. The integration with AWS Organizations further centralizes this reporting for enterprise-wide oversight and regulatory reviews.
