Webinar Description
Key Takeaways
- Survey findings from over 900 cybersecurity leaders reveal shifting priorities toward runtime security investments
- The “patch gap” emerges as a critical breach predictor as AI accelerates vulnerability exploitation timelines
- Traditional shift-left security strategies are proving insufficient against threats targeting production environments
- AI-powered applications introduce runtime visibility blind spots that existing controls struggle to address
- Designed for CISOs, application security leaders, security architects and DevSecOps professionals
Introduction
Miggo Security and the Cloud Security Alliance are presenting a webinar examining findings from the 2026 CSA survey of more than 900 cybersecurity leaders. The session addresses how artificial intelligence is fundamentally changing the threat landscape for application security teams, particularly in production environments where traditional pre-deployment controls offer limited protection. For security leaders grappling with accelerated exploitation timelines and evolving budget pressures, the research provides benchmarking data and strategic insights into where the industry is directing its defensive investments.
The timing reflects a broader industry reckoning with the limitations of shift-left security philosophies. While embedding security earlier in the development lifecycle remains valuable, organisations are discovering that vulnerabilities reaching production still represent significant exposure windows. When AI-powered tools enable attackers to weaponise disclosed vulnerabilities within hours rather than weeks, the gap between patch availability and deployment becomes a measurable breach predictor.
About This Event
This virtual webinar takes an executive-level, research-driven approach rather than offering hands-on technical training. The format centres on discussion and benchmarking, presenting real-world incident data alongside survey findings to illustrate how security programmes are adapting to AI-driven threats. Registrants receive access to a recording, allowing security teams to review the material and share insights with colleagues who cannot attend the live session.
The Cloud Security Alliance brings established credibility in cloud security research and standards development, while Miggo Security contributes expertise in runtime application protection. This combination positions the session to address both strategic considerations for security leadership and practical implications for teams responsible for protecting production workloads.
The Patch Gap as a Breach Predictor
Central to the survey findings is the concept of the “patch gap”—the interval between when a vulnerability becomes known and when organisations successfully remediate it across their environments. This window has always represented risk, but AI is compressing the attacker side of the equation dramatically. Automated reconnaissance, exploit generation and attack execution mean that disclosed vulnerabilities face weaponisation attempts far more quickly than in previous years.
The research suggests that organisations can use their patch gap metrics as a meaningful predictor of breach likelihood. Environments with extended remediation timelines face disproportionate exposure, particularly for vulnerabilities affecting internet-facing applications. This finding has significant implications for how security teams prioritise remediation efforts and allocate resources between prevention and detection capabilities.
Why Shift-Left Alone Falls Short
The shift-left movement encouraged organisations to address security concerns earlier in the software development lifecycle, integrating scanning, code review and security testing into development pipelines. This approach has delivered genuine improvements in code quality and reduced the volume of vulnerabilities reaching production. However, the survey data indicates that shift-left investments alone are not reducing overall exposure as much as organisations expected.
Several factors contribute to this gap. Modern applications increasingly incorporate third-party components, open-source libraries and AI-powered features that introduce risks difficult to assess before deployment. The behaviour of AI models in production can differ substantially from testing environments, creating blind spots that static analysis and pre-production testing cannot fully address. Additionally, the complexity of cloud-native architectures means that configuration issues and runtime interactions often create vulnerabilities that only manifest in production contexts.
This does not suggest that shift-left practices lack value—rather, that they represent one component of a comprehensive security programme rather than a complete solution. The survey findings point toward a rebalancing of investment between pre-production controls and runtime protection capabilities.
AI-Powered Applications and Runtime Visibility Challenges
The proliferation of AI-powered applications introduces distinct security challenges that the webinar addresses directly. These applications often exhibit non-deterministic behaviour, making traditional security monitoring approaches less effective. When application responses vary based on model inference rather than predictable code paths, distinguishing malicious activity from legitimate but unusual behaviour becomes considerably more difficult.
Agentic AI systems—those capable of taking autonomous actions based on their reasoning—compound these challenges further. Such systems may interact with external services, modify data or trigger downstream processes in ways that security teams struggle to anticipate or monitor effectively. The survey explores how organisations are approaching risk management for these emerging application architectures, including detection strategies and response procedures adapted for AI-specific threat scenarios.
Runtime visibility becomes essential in this context. Without detailed observability into how AI-powered applications behave in production, security teams cannot identify anomalies, investigate incidents effectively or validate that protective controls are functioning as intended.
Evolving Security Budgets and Investment Priorities
The survey captures a notable shift in how CISOs and security leaders are allocating their budgets. Investment intent is increasingly directed toward runtime security capabilities, reflecting recognition that production environments represent the primary breach battlefield. This trend appears across organisations in technology, financial services and healthcare—sectors managing substantial production risk in large-scale, AI-driven environments.
For security leaders seeking to justify runtime security investments to executive leadership and boards, the survey data provides useful benchmarking. Understanding how peer organisations are responding to similar challenges helps frame budget requests in terms of industry-standard practices rather than speculative spending. The webinar discusses how to translate technical risk into business terms that resonate with non-technical stakeholders.
Who Should Attend
The session targets senior security professionals responsible for application security strategy and implementation. CISOs and VPs of Security will find value in the benchmarking data and strategic framing, while Directors and Heads of Application Security can apply the findings to programme development and resource allocation decisions.
Security Architects, particularly those focused on cloud environments, will benefit from the discussion of runtime visibility challenges and AI-specific threat scenarios. DevSecOps and Product Security Engineers gain insight into how their pre-production work connects to runtime protection requirements, while Security Analysts can better understand the detection and response considerations for AI-driven threats.
Organisations managing complex production environments with AI components stand to gain the most from the research findings, though the strategic insights apply broadly to any security programme grappling with accelerated threat timelines and evolving application architectures.
Conclusion
The 2026 CSA survey arrives at a moment when security leaders face genuine uncertainty about how to adapt their programmes to AI-accelerated threats. The research offers empirical grounding for decisions that many organisations are already contemplating—whether to increase runtime security investments, how to measure and communicate production risk, and what capabilities matter most when traditional controls prove insufficient. For security professionals seeking to benchmark their programmes against industry peers and understand where the field is heading, the webinar provides a structured examination of these evolving challenges.