Sumo Logic today released its 2026 Security Operations Insights report, which found that security is complicated by a growing number of cloud tools, sprawling tech stacks and a lack of communication that leads to less reliability for security teams.
Security is becoming increasingly complex for enterprise organizations, as application environments are changing rapidly. AI hype has created a rush to develop and adopt AI tools while broadening the attack surface and forcing organizations to reconsider whether their security solutions are actually providing value.
The Sumo Logic 2026 Security Operations Insights report surveyed more than 500 IT and security leaders and was developed with independent research firm UserEvidence. Key findings include:
- 90% of security operations leaders say supporting data sources from multi-cloud and hybrid-cloud environments is very or extremely important for their SIEM, highlighting the continued need for data pipeline management.
- Only 51% say their current SIEM is very effective at reducing mean time to detect and respond to threats. And just 52% are very confident their current SIEM can scale to meet future security and cloud operations needs.
- 90% of security leaders say AI/ML is extremely or very valuable in reducing alert fatigue and improving detection accuracy. Yet their most common AI use cases focus on basic tasks like threat detection. These findings indicate that AI adoption isn’t as widespread through advanced security workflows as marketing narratives often suggest.
- 93% of enterprise organizations use at least three security operations tools, and 45% use six or more. It’s no surprise that over half (55%) of respondents report having too many point solutions in their security stack.
- 80% of enterprise organizations say security and DevOps use shared observability tools, but only 45% say the two teams are very aligned on tooling and workflows. 100% say a unified platform for logs, metrics, and traces would be valuable for their security and DevOps teams.
- 70% of respondents say they’ve fully or mostly automated their threat detection and response process, with 25% reporting it’s fully automated. Those who rely on a mostly or fully manual process are in the extreme minority.
These findings underscore that enterprise security leaders are overwhelmed. As AI continues to complicate the threat landscape, it adds yet another technology that needs to be monitored, secured, and used in security. The solution isn’t a larger security tech stack with more siloed tools. Instead, it’s a unified platform that acts as a single source of truth for DevSecOps, providing real-time insights and visibility across the entire environment.
Resources
MIND Announces Autonomous DLP for Agentic AI
Posted in Commentary with tags MIND on January 28, 2026 by itnerdEnterprises are moving quickly to adopt agentic AI to drive real business outcomes, including faster decision-making, increased productivity and new operational efficiencies. But as AI systems become more autonomous, those outcomes depend on one critical factor: whether organizations can trust how their data is accessed, used and controlled.
Today, MIND announced DLP for Agentic AI, a data-centric approach to AI security designed to help organizations safely achieve the business value of agentic AI by ensuring sensitive data and AI systems interact safely and responsibly.
Agentic AI can autonomously create, access, transform and share data across SaaS applications, local devices, homegrown systems and third-party tools. While this unlocks meaningful gains in speed and scale, it also introduces new risks. Without clear visibility and controls, data security gaps can undermine AI initiatives, slow adoption and put business outcomes at risk.
Data Security as the Foundation for AI Outcomes
As organizations evaluate how to secure agentic AI, new security categories are appearing. However, most of these emerging approaches fail to secure the critical foundation that Agentic AI relies on: the data itself.
MIND’s DLP for Agentic AI starts with the belief that business outcomes depend on whether AI systems have the right access to the right data at any point in time. Instead of securing models or reacting to outputs, MIND ensures sensitive data is understood, governed and protected before any AI agent can access or act on it.
With this data-centric approach, organizations can:
By putting data security and controls at the center of AI adoption, MIND helps organizations turn AI potential into measurable business results with the right guardrails.
Customers are already using MIND to support enterprise AI initiatives and the secure use of GenAI while maintaining strong data security.
Built for an Agentic AI World
Traditional DLP programs were designed for predictable, human-driven workflows. Agentic AI operates differently, moving at AI speed and acting autonomously. MIND’s DLP for Agentic AI brings context-aware automation to data security, helping teams prevent risk before it impacts the business.
As organizations continue to invest in agentic AI, MIND positions data security and controls as the missing piece required to achieve AI-driven outcomes safely and sustainably.
To learn more about DLP at AI speed and how MIND enables secure, outcome-driven AI adoption, visit mind.io.
Leave a comment »