SolarWinds announced the launch of SolarWinds® AI to empower tech pros to manage the complexities of modern digital environments.
The purpose-built generative AI engine was developed using the company’s newly unveiled AI by Design framework to help ensure privacy, security, and reliability in developing advanced AI technologies.
SolarWinds AI is first being introduced in SolarWinds Service Desk, the company’s IT service management (ITSM) solution for improving IT outcomes, service delivery, and employee satisfaction. SolarWinds AI powers a transformative new generative AI feature in Service Desk, designed to supercharge agent workflows and facilitate empowered, high-performing IT teams to resolve tickets in minutes rather than hours or days. This accelerated remediation process helps create a world-class experience for employees across the organization by minimizing downtime and outages.
Powered by large language models (LLMs) and proprietary algorithms, SolarWinds AI in Service Desk instantly summarizes complex ticket histories, provides suggested agent responses to inquiries, and generates real-time recommended steps for resolving issues. The company also plans to introduce SolarWinds AI throughout its suite of observability and IT management solutions, adding a new digital partner for DevOps, SecOps, database teams, and beyond.
“In creating SolarWinds AI, we leveraged our long history of engaging with the IT community to build an AI system that could act as a valued partner to IT pros in their everyday lives and reimagine the productivity, efficiency, and innovation they’re capable of,” said Krishna Sai, SVP of Engineering at SolarWinds. “We believe that our new AI by Design framework sets a model for how the broader industry should approach AI, built on our belief that privacy, security, and trust must be inherent to any AI system.
AI by Design is a rigorous framework for creating secure, productive, and enduring AI-driven solutions, guided by four key principles:
- Privacy and Security: To help ensure unparalleled data safety, SolarWinds AI is designed to secure personal data collection, storage, use, and protection through robust measures, like role-based access control (RBAC), multi-factor authentication (MFA), privileged access management (PAM), and anonymization techniques. Sensitive customer data does not pass through external large language models (LLMs), and SolarWinds AI improves based on usage patterns and organizational needs without compromising data privacy.
- Accountability and Fairness: SolarWinds AI proactively includes humans in training and testing to oversee AI-guided decision-making processes to avoid biases. It captures and addresses negative user experiences through robust feedback and validation mechanisms, ensuring that human judgment plays a crucial role throughout the AI lifecycle.
- Transparency and Trust: To provide confidence in the outcomes of AI-driven processes, SolarWinds AI features a well-defined explainability pipeline to demonstrate the rationale behind AI-generated actions. This enables continuous refinement based on the customers’ evolving needs and helps demystify the process for AI decisions and outputs.
- Simplicity and Accessibility: SolarWinds AI is built into the company’s user-friendly product interfaces to help ensure the continuation of regular workflows and existing user procedures, behaviors, and practices.
The Latest
While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...
Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...
For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...
For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...
Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...
Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...
Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...
Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...
AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...
More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...