Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Understanding your Tines deployment options

At Tines, we understand different systems and environments require different deployment options. Some organizations require extra guardrails to access and manage their systems and data. Those operating in regulated industries or the government sector often require self-hosted or on-prem solutions to ensure their networks are secure and compliant. Tines is unique in many ways, but one of our biggest differentiators is that our intelligent workflow platform can be deployed in the cloud or self-hosted.

Introducing your AI interaction layer

AI is everywhere, but without a consistent and secure way to connect it to real systems, it remains fragmented, difficult to govern, and hard to scale. Today, we’re introducing your AI interaction layer. Tines unifies AI agents, copilots, and Model Context Protocol (MCP) servers and clients in a single, secure environment. It gives teams a practical way to connect AI to systems and put it to work seamlessly across operations.

The Tines platform: a look back at 2025

Early in the year, we introduced multiple drafts for Change Control. This feature enables builders to work on the same project simultaneously, each within their own draft environment. The upside? Agents were the talk of the town in 2025. Tines CEO Eoin Hinchy shared his thoughts on how they could help end muckwork, and shortly after, we launched the AI Agent action.

The strain of reactive infrastructure reliability

Every IT Operations team knows the feeling: the alert storm hits, dashboards light up, and another late-night scramble begins. You fix the issue, document it, and brace for the next one. The pattern repeats; not because your team lacks skill or visibility, but because the systems you rely on don’t move as fast as the infrastructure they manage. Downtime doesn’t start when systems fail. It starts when signals go unanswered.

Turn Structured Data into Intelligent Action with Cribl and Tines

IT and security teams are stuck between two bad options: over-automate on noisy, incomplete data and risk eroding trust, or avoid automation and drown in manual triage. With surging data volumes and increasingly complex stacks, both choices drive alert fatigue, longer MTTD/MTTR, and analyst burnout. Tines and Cribl offer an alternative vision.

The secret to holiday resilience: offload the muckwork with intelligent workflows

Security and IT professionals know the pattern all too well: workplace stress peaks in the weeks leading up to major holidays. Teams face pressure to close out projects, meet year-end deadlines, and handle increased workloads with reduced staff. And to top it off, cyber threats don’t take holidays. In fact, attackers often exploit this exact window of vulnerability.

5 reasons patch management stalls and what modern IT teams can do to fix it

Patch management is one of those responsibilities everyone agrees is essential, yet very few teams feel confident about. The organizations I speak with every week are not struggling because they lack urgency or awareness. They are struggling because the environment around patching has changed dramatically.

Why I'm leading Tines' internal workflow transformation

I first met Tines co-founders Eoin Hinchy and Thomas Kinsella more than a decade ago at eBay. Even then, we shared the same frustration: too much important work was slowed down by brittle processes, manual handoffs, and disconnected tools. We all believed technology should help people focus on meaningful work, not slow them down in muckwork. That idea has shaped my career ever since. I started out in security operations, using automation to make my own job easier.

SOAR in the AI era: How SAP uses intelligent workflows to build an AI SOC

SOAR was created to help security teams work faster and more consistently by automating and orchestrating core security operations. It has always had to adapt to new and evolving technologies, but our current AI era has brought about a turning point. As cloud environments scale, manual playbooks can’t keep up. Now, it’s not enough to automate. We need systems that can understand the context they’re running in and adapt accordingly.