Fragmented AppSec, CloudSec, and SecOps processes operate in silos that create vulnerabilities. A unified, AI-driven platform can fill these gaps to mount effective cloud threat detection and response.
Modern enterprise security teams face an escalating crisis. Sophisticated cloud-based threats are taking advantage of divisions between the operational silos that divide their organizations. Specifically, Application Security (AppSec), Cloud Security (CloudSec), and Security Operations (SecOps) teams typically work in isolation using separate tools, workflows, and data sources, all of which creates dangerous vulnerabilities that attackers can systematically exploit.
The core issue plaguing enterprise security is fragmentation. CloudSec teams detect vulnerabilities in cloud infrastructure but lack runtime context to determine active exploitation. Meanwhile, SecOps analysts monitor alerts without visibility into the cloud services and applications they’re protecting. Finally, AppSec teams are disconnected from the risks that materialize in production environments. This disconnect becomes particularly dangerous given that 80% of critical exposures now occur in cloud environments, where attackers can move fluidly between infrastructure, enterprise systems, and application layers.
The challenges are numerous:
- Cloud-native environments introduce vulnerabilities that traditional security tools cannot adequately address.
- Visibility gaps from siloed teams obscure critical data, while confusion over shared responsibility between cloud providers and internal teams delays incident response.
- Dynamic environments with ephemeral resources create constantly shifting attack surfaces that overwhelm tracking capabilities.
- Risk prioritization becomes nearly impossible under the weight of thousands of generated alerts, while access management across proliferating identities leads to inconsistent policies and heightened breach risks.
- Configuration management remains problematic, with misconfigurations a top cause of incidents.
- Massive telemetry volumes obscure critical risks, preventing teams from identifying potential breach paths effectively.
Organizations also suffer significant operational burdens from managing a sprawling array of fragmented security tools. Teams spend valuable time manually correlating data across disconnected platforms, delaying investigations and responses to ever more sophisticated AI-driven attacks. Concurrently, training requirements multiply as each tool requires specialized expertise, straining already limited resources.
While cloud-native application protection platforms (CNAPPs) have advanced from basic cloud security posture management to include container security and data protection, they face critical limitations. Current CNAPPs lack integration with essential telemetry sources like endpoint detection and threat intelligence platforms, which means teams must manually correlate this information, slowing the investigation. Also, traditional CNAPPs’ automation capabilities remain constrained to cloud-specific tasks, which creates gaps in ecosystem-wide threat response. Most significantly, they provide incomplete attack chain visibility, because they cannot account for indicators of compromise occurring beyond their designated scope.
Modern cloud security requires a fundamental architectural shift toward AI-driven security operations built on unified data and automation. This approach integrates data from a dynamically stitched dataplane that includes code supply chains, cloud configurations, identity systems, network endpoints, and threat intelligence sources. AI can then identify attack paths, prioritize critical risks, and recommend precise remediation actions across the entire security ecosystem, all while continuously learning from each incident to further strengthen security.
Cloud detection and response (CDR) is a critical capability within this unified framework, providing security teams with deep visibility into cloud-native applications, automated threat detection, and coordinated response capabilities across cloud environments. CDR enables real-time threat detection and automated response through intelligent playbooks.
Organizations implementing unified security solutions can achieve remarkable operational improvements. They resolve 90% of alerts through automation while achieving 10-second mean-time-to-detect and 1-minute mean-time-to-respond. These solutions enable accelerated cloud service deployment with built-in security, lower total cost of ownership through tool consolidation, and enhanced productivity across all security teams.
The strategic imperative is clear: Successful organizations are abandoning point tool proliferation in favor of unified cloud security models that eliminate artificial barriers and enable proactive, AI-driven defense across their entire attack surface.
Download the full report for a deeper dive into these issues and strategies for solving them.
