January 2026 Summaries

AI coding assistants have become mainstream, with 87% of organizations adopting tools like GitHub Copilot, Cursor, or Claude Code, though their integration presents both productivity benefits and security challenges. While concerns persist that AI-generated code may be more vulnerable, the narrative overlooks the fact that these tools often produce more consistent and secure code for standard implementations than inexperienced developers might. However, the rapid development velocity enabled by AI tools creates significant challenges for Application Security (AppSec) programs, including a lack of developer context, overwhelmed manual processes, and the emergence of new attack surfaces. As AI accelerates development and introduces components like chatbots and AI-powered features, AppSec stakeholders face difficulties in managing and securing the expanding attack surface. To address these challenges, organizations must shift from traditional approaches to a model focusing on visibility, runtime validation, and intelligent prioritization of vulnerabilities to effectively manage the risks associated with AI-assisted development.

In July 2023, the SEC introduced significant changes in cybersecurity accountability for public companies, requiring disclosure of material cybersecurity incidents within four business days of determining their materiality. Annual disclosures must now include details about risk management processes, board oversight, and the effectiveness of cybersecurity programs, with compliance deadlines starting in December 2023 and enforcement beginning in 2024. Application security teams are tasked with demonstrating systematic risk management processes to identify and manage cybersecurity threats, emphasizing the need for proactive measures like regular testing and maintaining visibility over the attack surface. StackHawk assists companies in meeting these requirements by integrating security testing into CI/CD pipelines, ensuring vulnerabilities are detected and addressed before reaching production, and providing comprehensive documentation and metrics to support compliance. This shift-left security strategy helps reduce the likelihood of incidents requiring disclosure, facilitates informed materiality assessments, and supports board-level oversight and regulatory scrutiny, thereby demonstrating a robust and consistent cybersecurity posture.

The 2026 State of AppSec survey by StackHawk highlights that 68% of organizations lack full visibility into their API attack surface, with AI-accelerated development outpacing security capabilities. APIs, representing the majority of web traffic, are increasingly targeted by attackers exploiting vulnerabilities such as broken authentication and business logic abuse. Traditional security tools are inadequate for modern API environments, necessitating purpose-built API security platforms that integrate into development cycles for real-time visibility and proactive issue resolution. The guide reviews the top five API security solutions of 2026, emphasizing the importance of automated discovery and real-time testing to prevent vulnerabilities from reaching production. Compliance with frameworks like PCI DSS v4.0.1 and the EU Cyber Resilience Act mandates continuous API security testing. Tools like StackHawk, Akamai, Snyk, Noname Security, and SALT Security offer various capabilities including dynamic application security testing, DDoS protection, supply chain vulnerability scanning, and AI-powered threat detection, catering to different stages of API lifecycle management and organizational needs.

AI-driven development has rapidly become a mainstream practice, challenging application security (AppSec) teams to adapt to new realities where AI coding assistants like GitHub Copilot are widely adopted by 87% of organizations, though the security implications of AI-generated code remain debated with mixed perceptions of risk. AppSec teams, often composed of four or more members and spanning various industries, are struggling with the balance between maintaining development velocity and ensuring security, as AI-driven coding increases code volume significantly without a corresponding increase in security resources. Despite the widespread use of security testing tools, such as Software Composition Analysis and API Protection, teams face challenges in addressing new risks like AI-specific vulnerabilities and are spending substantial time triaging issues, leading to alert fatigue. The growing complexity and speed of development have created visibility and accountability gaps, with boards increasingly demanding insights into risk postures, yet teams often report activity metrics rather than risk-oriented metrics. To address these challenges, organizations are investing in AI/LLM security strategies, emphasizing the need for better visibility, runtime testing, and a shift towards risk-based metrics to provide a clearer understanding of security posture and effectiveness in the evolving AppSec landscape.

PCI DSS v4.0.1 introduces a shift from periodic audits to continuous, proactive security measures for organizations handling cardholder data, with new requirements becoming mandatory by March 31, 2025. This updated standard emphasizes ongoing compliance throughout the software development lifecycle, including application security, API testing, and pre-production vulnerability detection. Traditional approaches relying on annual penetration tests and quarterly scans are now considered outdated. StackHawk's shift-left Dynamic Application Security Testing (DAST) approach aids compliance by embedding security validation within CI/CD pipelines, ensuring vulnerabilities are addressed before production. This method aligns with v4.0.1's requirements for maintaining software and API inventories, tracking changes, and prioritizing critical vulnerability remediation within 30 days. The new standards also stress the need for comprehensive and continuous testing, relevant security training for developers, and robust audit trails to prove compliance activities. StackHawk's integrated approach with partner tools offers a layered security strategy, supporting the entire development lifecycle and allowing organizations to achieve compliance without compromising development speed.

Metrics play a crucial role in securing funding and demonstrating the value of Application Security (AppSec) programs, particularly as AI-driven development increases code velocity. Many Dynamic Application Security Testing (DAST) programs fail not due to technological shortcomings but because they cannot effectively convey their impact on speed, incident reduction, and risk management. A successful metrics framework should address whether the testing is pertinent, if it genuinely reduces application risk, and if it scales effectively. This framework is especially vital for DAST due to its higher demands for infrastructure and coordination compared to static analysis. Key metrics include coverage and adoption, risk reduction, and efficiency and health, which respectively assess whether the right applications are tested, whether vulnerabilities are mitigated pre-production, and whether the program is sustainable without excessive effort from AppSec teams. By aligning metrics with business outcomes and showing trends rather than isolated figures, AppSec programs can illustrate their progress and justify their continued investment. As programs mature, they should evolve their metrics to reflect broader impact and efficiency, ensuring that metrics not only measure but also enhance the program's effectiveness.

Burp Suite, once a preeminent tool for web application security testing, faces challenges in adapting to modern development practices characterized by rapid code deployment and AI-assisted development. Its Java-based architecture leads to significant performance issues, such as high memory consumption and slow scan times, which create bottlenecks in fast-paced environments. The platform's steep learning curve and integration limitations with modern DevSecOps workflows further exacerbate its usability for developers, who often find it difficult to incorporate security testing into their existing CI/CD pipelines. As a result, many teams are turning to alternatives like StackHawk, Zed Attack Proxy (ZAP), and Invicti, which offer faster setup and scans, automated API discovery, and better integration with developer workflows, addressing the shortcomings of Burp Suite and aligning security testing with the velocity of contemporary software development.

Dynamic Application Security Testing (DAST) traditionally performed by security teams in production environments must evolve to integrate more effectively into developer workflows to keep pace with modern development practices. True shift-left DAST requires integration into continuous integration/continuous deployment (CI/CD) systems, fast runtime testing, and actionable findings for developers, but it is more complex to implement than Static Application Security Testing (SAST). This complexity results from the need for setting up test environments, configuring authentication, and tuning for specific APIs, which often causes DAST initiatives to stall. However, DAST identifies critical vulnerabilities like authentication bypasses and runtime injection flaws that SAST misses, making it essential for effective security. To scale DAST, organizations should create a "paved road" with standardized templates, comprehensive documentation, and repeatable processes that allow development teams to self-onboard without custom configurations. This strategy includes defining testing requirements, integrating tests into developer workflows, and establishing governance and infrastructure support. The goal is to embed security testing seamlessly into the software development lifecycle, allowing teams to fix vulnerabilities efficiently and scale security efforts without proportionally increasing headcount. By building such systems, organizations can enhance their application security posture even as development accelerates with AI and other technologies.