2026年云安全现状调研报告：AI 如何重塑云安全运营

The State ofCloud Security 2026 Signal vs. Noise: How AI Is ReshapingCloud Security Operations What 633 cybersecurity professionals across nine countries say about the gap between AI’s promise andoperational reality — and what it means for security teams in 2026. Contents Executive Summary Chapter 1: The State of Cloud Security Operations Chapter 2: The Cloud Platform Landscape Chapter 3: The AI Adoption Gap Chapter 4: The Visibility Crisis Chapter 5: Where AI Can Actually Help Chapter 6: What Will Separate Leaders from Laggards Chapter 7: The Case for AI-Native Cloud Security MethodologyAbout Prowler Executive Summary Cloud security is no longer a standalone concern — it’s the backbone of modern defense. And expectationsfor AI-powered protection are rising fast. Vendors promise autonomous protection, instant detection, andseamless remediation. But what do security teams actually need? To cut through the hype, Prowler surveyed 633 cybersecurity professionals across nine countries aboutwhat they actually need from cloud security in 2026. The findings reveal a workforce under immense strain,a widening gap between AI ambitions and adoption, and a clear set of priorities that should shape everyvendor’s roadmap. The question isn’t whether AI will reshape cloud security. It’s whether it will reduce noiseor amplify it. This report isn’t just a snapshot of the market. It’s a blueprint for what AI-native cloud security mustbecome: contextual, transparent, community-informed, and operationally grounded. Key Findings at a Glance The core insight: Detection is no longer the hard problem. What’s broken is everythingthat happens after the finding shows up: context gathering, re-triage, lost institutionalknowledge, and the manual toil of stitching siloed data together. Chapter 1 The State of CloudSecurity Operations How weekly incidents, skills shortages, and compliance complexity are overwhelmingsecurity teams — and why more tools aren’t the answer. The State of Cloud Security Operations Security teams are drowning. Not because they lack tools, but because the tools they have generate morework than they resolve. The average security team now handles 71 incidents per week — more than 3,600every year — and more than a quarter of respondents say they spend over half their time on low-valuemanual tasks like triaging alerts, gathering context, or assembling compliance evidence. This isn’t a technology problem. It’s an operational architecture problem. Security engineers have become,in effect, human integration layers — flipping between fifteen tabs, trying to piece together whether afinding actually matters in the context of their environment. That’s not security work. That’s data assembly.And it’s burning people out. Consider what a typical cloud security engineer’s morning actually looks like. Their CSPM flags a publiclyexposed S3 bucket. Their identity governance tool surfaces an IAM role with overly broad AssumeRolepermissions. Their container security scanner detects a critical CVE in a base image running in EKS. TheirSIEM correlates an anomalous API call pattern from an EC2 instance in a production VPC. Each of thesefindings lives in a different console, with different severity scoring, different context, and no sharedunderstanding of how they relate to each other. The engineer has to manually correlate these signals: Does that IAM role have a trust policy that could bechained with the exposed bucket? Is the vulnerable container image actually reachable from the internet,or is it behind a private subnet with restrictive security groups and NACLs? Is the anomalous API activityrelated to the overprivileged role, or is it a legitimate workload pattern? Answering these questions requirestoggling between AWS Console, CloudTrail logs, VPC flow logs, Kubernetes RBAC policies, and half a dozenthird-party dashboards. The tools find problems in isolation. The human stitches the story together. The Strain Isn’t Distributed Evenly The survey reveals sharp divides in how teams experience this pressure. Companies with fewer than 100employees were 33% more likely to automate fewer than 10% of their security incidents. Meanwhile, CISOswere 73% more likely to report automating 50–75% of incidents — showing that automation maturity isbeing driven from the top, but is failing to trickle down to the teams that need it most. Smaller organizations remain stuck in manual workflows, creating an efficiency drain for the teams that canleast afford it. While larger enterprises have begun investing more heavily in automation, the gap betweenstrategy and execution remains wide across the board. What’s Actually Slowing Teams Down When asked about their biggest operational challenges, the answers were consistent across geographiesand company sizes: 42% Skills Shortage Compliance Burden Limited Automation AI and cloud security talent gaps Growing regulatory complexity Gaps in detection, triage, remediation These