The cybersecurity industry is rethinking how risk is managed at scale, and Brinqa leaders are part of that conversation in Solutions Review’s 2026 Cybersecurity Predictions.
The article brings together predictions from more than 140 contributors across the cybersecurity ecosystem, including CISOs, security executives, analysts, and technology leaders from organizations such as Microsoft, Palo Alto Networks, Qualys, CyberArk, Zoom, and leading enterprise security teams. Together, these perspectives outline the forces shaping cybersecurity in 2026 – from AI-driven operations to the growing importance of data quality, context, and decision intelligence.
Brinqa was represented by two members of its executive leadership team:
Dan Pagel, CEO: AI Forces a Data Reckoning
Pagel’s prediction focuses on how AI will expose structural weaknesses in security programs, and accelerate those built on strong data foundations:
“AI is about to force a reckoning in cybersecurity. Not because it’s ‘smart,’ but because it’s fast – and when speed is paired with transparency, it exposes everything.
The real shift in 2026 will be AI stepping into the operator role… This only works if the underlying data is structured, complete, and continuously updated.
2026 is the year cybersecurity stops being a tooling problem and becomes a data and decisioning problem. And that’s long overdue.”
Brad Hibbert, CSO & COO: Exposure Management Becomes Agentic
Hibbert predicts that 2026 will mark a turning point in how exposure management programs operate, driven by the need to scale beyond human-limited processes:
“2026 will be the year exposure management shifts from reactive reporting to truly agentic intelligence.
Security teams continue to struggle with incomplete data, conflicting sources, cloud sprawl, nonstop change, and an overwhelming volume of security signals… The foundational problems of data quality, noise, and complexity cannot be solved at the human scale.
By the end of 2026, the most successful programs will rely on agentic AI as the engine of trustworthy and continuous exposure intelligence… This marks the transition from exposure visibility to exposure intelligence, and ultimately toward exposure autonomy.”
Together, these perspectives reinforce a common industry theme echoed throughout the Solutions Review article: the future of cybersecurity will be defined less by tools and more by data integrity, contextual intelligence, and the ability to act decisively at scale.
“By the end of 2026, the most successful programs will rely on agentic AI as the engine of trustworthy and continuous exposure intelligence… This marks the transition from exposure visibility to exposure intelligence, and ultimately toward exposure autonomy.”
Brad Hibbert, CSO & COO, Brinqa
Read the full article on Solutions Review
Cybersecurity Predictions from Industry Experts for 2026
FAQs
The predictions focus on how cybersecurity programs are evolving to handle scale, complexity, and speed. Key themes include the growing role of AI in security operations, the importance of data quality and context, and the shift from reactive reporting toward decision-driven security models.
Agentic AI refers to systems that actively participate in security workflows rather than simply generating insights or visualizations. These systems can interpret context, reconcile incomplete data, maintain data accuracy over time, and support prioritization and remediation decisions at machine speed.
Agentic exposure management refers to an approach where AI systems actively participate in managing cyber exposure rather than simply reporting on it. Instead of relying on static dashboards or manual analysis, agentic systems help interpret context, reconcile incomplete or conflicting data, maintain data accuracy over time, and support prioritization and remediation decisions. The goal is to move from passive visibility to continuous, trustworthy exposure intelligence that can operate at a scale and speed beyond human-only processes.
Exposure intelligence incorporates context such as asset criticality, identity risk, and business impact to help teams understand which issues matter most. Unlike traditional vulnerability management, it focuses on decision clarity and measurable risk reduction rather than raw findings.
As organizations manage millions of assets and findings, incomplete or inconsistent data undermines prioritization and slows response efforts. The predictions emphasize that AI and automation only deliver value when built on structured, reliable, and continuously updated data foundations.
Many contributors predict a shift away from manual, tool-centric approaches toward intelligence-driven models where AI assists with validation, correlation, and decisioning, enabling teams to move faster without increasing workload.
