When the Cloud Blinks: Lessons from the AWS Outage

Cloud backup strategies and disaster recovery automation are driving resilience in the age of AI and ensuring business continuity.

Senior Director, Data and Analytics, LTM

When the Cloud Stopped for a Moment

Recently, AWS outage served as a stark reminder of the importance of cloud backup strategies and disaster recovery automation. Many IT professionals found themselves working tirelessly to restore their systems and businesses due to the disruption caused by the AWS outage. Amazon Web Services, the backbone of countless digital ecosystems, experienced a partial outage in its US-East region. The outage affected systems ranging from banking applications to entertainment platforms, but its impact was limited to the eastern parts of the US.

For many, this incident cut their Diwali celebrations short. Whether it happens during Diwali, Christmas, a busy holiday season, or any critical event, the common thread is an outage that disrupts businesses that are unprepared for such scenarios. A few enterprises that had planned effectively managed to recover within minutes, without waiting for AWS to rectify the issue. However, many others could only resume operations once AWS restored their systems.

Within minutes of this outage, our teams and clients were on calls, comparing notes, checking dependencies, and testing failovers. It was a real-time reminder that even the most trusted systems can blink without warning.

This incident reminded us that resilience is more than uptime; it’s about preparedness for such moments and much worse. This was only a blink. There were no catastrophic events.

Diagnosis of the Outage and Importance of Resilience

AWS faced a DNS resolution issue. It is more like you cannot reach the destination because the indexing or the addressing system got stuck. To simplify it further, consider a case where you know the name of a person, but you don’t know his address or phone number, which you’ve stored in your phonebook, and the phonebook becomes inaccessible.

Cloud outages, while infrequent, can create ripple effects across industries. The immediate financial impact varies by sector. However, the broader implication often lies in how such events test organizational preparedness and communication.

Most leading providers, including AWS, have robust recovery mechanisms that minimize disruption, but the episode served as a reminder that resilience extends beyond recovery time objectives and SLAs. It includes what their preparedness is for business continuity, how strong their foundation is, how teams collaborate in crisis, and how quickly normal operations are restored with confidence.

In many ways, the incident reaffirmed the importance of treating resilience as an organizational discipline. One that combines technology, process, and culture rather than a purely technical benchmark.

What if There is a Malicious Attack? Are We Prepared?

What is the typical pattern in malicious attacks? Hackers first attack the backup data, which is backed up from production.

Then, they delete the complete backup system, so the customer will not have a backup to restore. The following modus operandi is to encrypt the production data. So now, there is no scope for the enterprise to have a fallback. The main production is encrypted, for which the attackers ask for a ransom.

Sophos’ reports* document rising ransom demands and that a large share of surveyed organizations have paid (their 2024/2025 releases report payment rates around the high-30s to ~50% depending on the survey year and sample), except for some organizations, such as financial enterprises. It’s a compliance mandate for them to declare whether there was a ransomware attack and what happened after it. So, in this POV, another aspect I’d like to touch upon is that we’re assuming that this was not a malicious attack as well as cover the data angle.

Data Security

Multilayer backups are not necessary for each and every application. The starting point is protecting your crown jewel applications through effective cloud backup strategies, or what we call a minimum viable product (MVP) or a minimum viable company (MVC). Unfortunately, not many customers have even updated MVC and MVP, which is the first step towards data security and keeping systems up and running when encountering situations like a cyber-attack or even a blink like an outage.

Data as the Foundation of Security and Cloud Confidence

For the last two decades, there have been discussions on configuration management databases (CMDBs). These databases give information about your application, its infrastructure, and the underlying configuration items, ideally including SaaS services.

This event also emphasized that cloud backup strategies are about more than data storage; they are about building trust through transparency and observability. What enterprises now seek is something deeper: trust that this is validated, accurate, and observable. Recovery confidence emerges from transparency—by knowing where your data resides, how it’s replicated, and how quickly it can be verified and recovered after a disruption. You must have confidence in your ability to recover with integrity instead of just availability.

As a principle, I often tell clients: trust cannot be outsourced. It has to be engineered into every layer of the enterprise from architecture to governance. The relationship between enterprises and cloud providers must evolve into a partnership of shared accountability, where resilience is a co-owned outcome rather than siloed.

Trust cannot be outsourced. It has to be engineered into every layer of the enterprise from architecture to governance.

Checklist for Disaster Readiness

There is end-to-end dependency mapping from the business service to the last configurable item, whether it is a service, software, or hardware. If you change one, it impacts your entire business.
That accuracy for any customer environment, even if you have a 90 percent update, is an indication of a sound system. Ideally, we need a 100 percent update, especially when we deploy AI. Unfortunately, the needle has not moved in this area.
Do we have that completeness, where the system or service is hosted/sourced and running smoothly even during an outage? CMDB must be reliable to ensure this kind of completeness.

Aspects that the customer will look for during disaster readiness include:

Identification of crown jewel applications and keeping the data up to date
Complete configuration management of the system to know what has been affected
Full-stack observability pointing to the root cause

While the first four areas are well known, technical sovereignty is emerging as a key priority for enterprises seeking control over their digital resilience. When outages strike, an automation and orchestration platform can bring systems back swiftly, without relying heavily on third parties. Yet, many organizations still overlook detailed measurement and monitoring of their recovery time objectives, leaving critical dependencies untested until disruption hits.

Technical Sovereignty

If your complete application is running on third-party-provided platforms, your business is totally dependent on them. Is your business ready to accept this risk? Many are not. Technical sovereignty is a concept about developing an isolated environment that is not dependent on third-party providers to run your business.

It is a complete replica of your current IT system, but totally under your control, including access to know-how on the technology, data, and hosting. Hence, you can look at the hybrid disaster recovery (DR) strategy. You use an on-premises environment as DR in addition to the cloud. Now, this is an expensive value proposition, and an enterprise needs to evaluate the need for its technical sovereignty carefully and adopt at least a plan for their MVP.

Minimizing Downtime with AI and Disaster Recovery Automation

Artificial Intelligence is redefining how enterprises detect, respond to, and recover from outages. Machine learning models can now predict performance degradation before it cascades into failure, automatically shifting workloads or provisioning alternate resources.

During the AWS disruption, organizations with intelligent observability platforms saw faster stabilization. AI-driven incident management tools isolated dependencies and executed pre-trained remediation scripts. Hence, restoring essential services in minutes rather than hours.

Even when disruptions occur on platforms like AWS, services can be restored within minutes through intelligent DR automation powered by AI. A well-architected DR strategy infused with AI and supported by leading automation tools can significantly reduce recovery time and eliminate prolonged outages. For example, AI-based anomaly detection can proactively identify failure patterns and trigger automated failover workflows. At the same time, AI-driven orchestration tools can dynamically reallocate resources and reconfigure environments without manual intervention. A robust DR strategy infused with AI and supported by leading automation platforms can drastically reduce downtime and recovery time objectives (RTO).

DR Case Study: A Failover Strategy in Place Can Mitigate Such Situations

The AWS incident also underscored a geographical truth: the cloud is global, but its vulnerabilities are regional. One of our insurance clients has subsidiaries on both the East and West Coasts. So, when the systems on the East Coast were not running smoothly, they could quickly fallback to their cloud on the West Coast. However, this could only happen because their change management process was linked to disaster recovery automation. The change map process should be foolproof because we don’t know when a disaster will strike.

They were contemplating whether to delay a fallback or wait for AWS to communicate about recovery. Thus, the waiting period also becomes crucial to the business continuity plan.

The Next Frontier: Cloud, AI, and Digital Twins

The future of resilience lies at the intersection of cloud, AI, and digital twins. With enterprise observability tools powered by AI, outages can be detected and mitigated before they escalate. Using digital twins, i.e., a replica of the production environment, ensures accuracy and eventless change implementation, reducing downtime and improving recoverability.

Automation maturity

The maturity or progression from script-based automation to tool-based and AI-based automation highlights the shift from rule-based to learning systems that form rules autonomously.

Script based automation
Tool-based automation (rule-based)
AI automation

Yet, this frontier introduces new complexities. AI systems, while powerful, can hallucinate or misclassify incidents, triggering unnecessary remediations or overlooking critical failures. The paradox is clear: AI enhances resilience but also adds new layers of dependency.

AI will predict the next outage before it happens, but we still need humans who know what to do when it does. Depending on the CMDB maturity of your enterprise, how well you know your environment, and the behavior of the environment, you can be confident in going towards any of the three choices.

The CXO Playbook: Building the ‘Resilience Quartet’

Resilience is no longer an IT function; it has become a business agenda. For CEOs, CIOs, and heads of Infrastructure steering digital enterprises, resilience must be redefined as an interplay of architecture, intelligence, people, and policies.

Architect for Failure

Design systems with the assumption that failure will happen. Build multi-zone, multi-region, and vendor-agnostic architectures that prevent cascading disruptions.

Anchor in AI-Powered Automation

Use AI-driven disaster recovery to detect, respond, and verify recovery integrity in real time. Embed trust frameworks for data lineage and validation at every layer.

Align Your People

Technology can fail. People must not. Build institutional readiness through simulation drills, playbooks, and cross-functional collaboration. Train teams to think in probabilities, not certainties.

Address policy control

Define the complete policy or SOP of your business continuity plan (BCP), the ready waiting period, and the authority that decides on immediate actions in case of disaster.

Technology can fail. People must not. Build institutional readiness through simulation drills, playbooks, and cross-functional collaboration. Train teams to think in probabilities, not certainties.

Resilience comes from how fast people adapt when the system breaks. The most advanced failover script cannot replace the intuition of a team that’s prepared, informed, and empowered.

When the Cloud Blinks Again

Outages are inevitable. Complexity guarantees that. But the loss of trust is not. Every disruption is an opportunity to stress-test assumptions, strengthen architecture, and reaffirm the human capacity for response.

The cloud will blink again. As leaders, we are responsible for ensuring that the business doesn’t lose sight when such a blink happens. Next time, the CEOs, CIOs, and heads of infrastructure won’t flinch with the right level of preparedness and will exactly know when their systems will start running again, in say 30 minutes or two hours. Hence, there is good visibility for continuing their business. This incident was a momentary lapse in the machine, but a lasting lesson for the people who build, depend on, and lead in the digital economy.

It reminded us that resilience is all about preparedness for failure and the restorability of functions that the CEOs, CIOs, and heads of infrastructure depend on, rather than uptime of the environment