Firefly AI announced the launch of its Cloud Resilience Posture Management (CRPM) solution.
Designed to help enterprises take a proactive approach to cloud outages, cyberattacks, and human error, Firefly’s CRPM makes resilience measurable, automated, and continuous by providing a full readiness score of all cloud assets to ensure restore capabilities for critical functions.
Firefly’s CRPM provides a holistic view of recovery readiness. It assesses which cloud data-stores are backed up, whether the underlying infrastructure can be rebuilt, and identifies where resiliency is threatened by reliance on a single region or service.
“Every enterprise knows that it's not a question of if, but when another outage is coming,” said Ido Neeman, CEO and Co-Founder of Firefly. “Outages happen all the time, and while not all of them are newsworthy, each incident costs companies millions in lost revenue, productivity, and customer trust. Firefly is redefining what it means to be cloud-ready with CRPM that creates genuine resilience. We are giving enterprises the ability to recover their operations within minutes instead of waiting for the cloud provider to fix the problem.”
CRPM incorporates concepts from Cloud Security Posture Management (CSPM) and combines them with the intelligence of Cloud Automation to create a unified resilience layer that turns recovery into a proactive, measurable posture rather than a reactive scramble. For executives managing resilience at scale, Recovery Time Objective (RTO) has become a defining metric. Firefly’s CRPM creates granular visibility into asset restore readiness, empowering organizations to audit, assess, and improve their RTO.
The Latest
In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...
Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...
Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...