Ayehu launched its next generation automation and orchestration platform for IT and Security operations.
The new platform is Software-as-a-Service (SaaS)-ready for hybrid deployments and is powered by artificial intelligence (AI) and machine learning driven decision support, for fully enhanced and optimized automated workflows.
“We’ve received overwhelmingly positive initial feedback from our partners and customers who have previewed our new platform and are excited to now make it generally available,” said Gabby Nizri, Co-founder & CEO of Ayehu. “We developed it because we wanted to make it even easier for our customers to incorporate and use automation as a game changer in their business. The SaaS-ready, multi-tenant platform is now able to deliver efficiencies across hybrid environments. This sets the stage for CIOs around the world to start the journey to enable the Self Driving Enterprise.”
The platform includes an architecture redesign to support managed service providers (MSP) and businesses with hybrid deployments across on-premise, private and public cloud environments such as AWS and Azure. It also enriches product security in areas such as message encryption across internal and external networks and presents a brand new user interface.
Key features include:
- AI Powered – Machine learning delivers decision support via prompts to optimize workflows and dynamically creates rule-based recommendations, insights, and correlations
- SaaS Ready – Ideal for hybrid deployments, supports multi-tenant, communication encryption, OAuth2 authentication, and internal security improvements
- High Availability and Scalability – Ayehu easily scales to support organizations with a high volume of incidents and safe guards against a single-point-of-failure
- Workflow Version Control – Ayehu is the first IT automation and orchestration platform to provide version control on workflows, allowing users to rollback changes and review, compare or revert workflows
- Tagging and Labeling – Ayehu users can associate workflows with keywords through tags to quickly search and return commonly used workflows
- User Interface Enhancements – The new angular 2.0 web-based interface, offering easy and user-friendly workflow designer and template navigation, as well as white labeling options for OEM partnerships
Ayehu acts as a force multiplier, driving efficiency through a simple and powerful IT automation and orchestration platform powered by AI. The next generation automation platform helps enterprises save time on manual and repetitive tasks, accelerate mean time to resolution (MTTR), and maintain greater control over IT infrastructure. IT and security operations teams can fully- or semi-automate the manual response of an experienced IT or security operator/analyst, including complex tasks across multiple, disparate systems. Ayehu’s response time is instant and automatic, executing pre-configured instructions without any programming required, helping to resolve virtually any alert, incident or crisis.
The Latest
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...