Shifting from Vulnerability Management to Security Risk Prioritization with AI

The adage ‘an ounce of prevention is better than a pound of cure’ applies to AppSec vulnerability management. Traditionally, AppSec has focused on a reactive ‘curing flaws’ paradigm, identifying and fixing vulnerabilities after they have occurred. However, the never-ending escalation between threats and security leads to alert fatigue and security debt. This contrasts sharply with a proactive, security risk prioritization approach designed to prevent vulnerabilities from entering the codebase in the first place. This approach emphasizes integrated visibility, automated processes and AI utilization, and direct correlation of threats to business impacts. 

Choosing Security Risk Prioritization Over Exhaustion 

Today, apps are built faster than ever, thanks to AI-generated code, vibe coding, and reusable third-party software components. However, security flaws accumulate faster than security teams can remediate them. When high-severity flaws remain unremediated for over a year, they become critical security debt. 

Our 2025 State of Software Security (SOSS) report corroborates this trend of increasing critical security debt. The report shows that half the organizations surveyed suffer from critical security debt. Furthermore, 70% of this critical security debt arise from third-party software components that require constant monitoring and changes to policy. 

Manual security tests and best guess remediation don’t keep pace with modern development and don’t create meaningful traction in eliminating critical vulnerabilities. To effectively tackle this growing security debt, it’s essential to transition from a reactive ‘curing’ approach to a proactive, preventive AppSec strategy. This involves preventing vulnerabilities from entering the codebase in the first place. To differentiate this approach from conventional vulnerability management, we suggest taking a security risk prioritization approach or developing a security risk prioritization strategy. 

Security risk prioritization minimizes toil across teams by offering: 

• Clean code at the source: Investing in software composition (SCA), static analysis (SAST), and Package Firewall to prevent, find, and fix vulnerabilities where developers spend most of their time: in the IDEs, third-party components, APIs, and OSS frameworks your team uses.  
• AI-Powered remediation: Fixing critical security flaws with AI-generated remediation suggestions to reduce the burden on development and security teams. 
• Runtime testing: Performing regular DAST scans to identify flaws that can only be identified at runtime. 
• View everything in context: pull all findings into a single, authoritative version of truth to understand the origin, owner and root cause of risk using Veracode Risk Manager. 

Contextual Prioritization Promotes Risk-Based Security Strategy 

A risk-based security strategy prioritizes remediation based on a number of factors, including severity, priority, policy, and business context. For instance, a flaw in an isolated test environment is far less critical than a vulnerability in a production system that may compromise sensitive user information​.

Many organizations rely on severity scores alone to prioritize vulnerabilities. But this one-dimensional approach often fails because risk is complex and context-specific. Here’s why it breaks down: 

• Lack of effective prioritization: Security teams face alert fatigue and information overload, making it hard to focus on what truly matters. Without contextual risk scoring, this leads to analysis paralysis. 
• Tool and context gaps: Critical context – like asset value, exploitability, and business impact – is often fragmented across tools, making it difficult to assess real-world risk. 
• Remediation bottlenecks: High volumes of low-context alerts create massive backlogs, stalling remediation efforts and reducing confidence in the overall process. 

VRM addresses these gaps by analyzing hundreds of risk factors to stack rank vulnerabilities based on real-world context, not just raw severity. 

Security Risk Prioritization Reduces Manual Effort 

Conventional AppSec practices create backlogs and slows down the software development lifecycle (SDLC). Security risk prioritization, when implemented well, accelerates SDLC throughput by reducing manual effort. 

Forrester’s Total Economic Impact (TEI) study reveals that organizations using Veracode Risk Manager to consolidate security risks across multiple tools, report a 75% reduction in risk of software-based attacks​. Also, using Veracode improved developer productivity by 80%, unlocking over 70,000 developer hours from manual AppSec tasks. 

Additionally, organizations using Veracode Fix, a remediation tool that uses AI to automate vulnerability fixes, were able to patch up to 74% of Java security findings without requiring developers to write any new code​. 

Veracode Helps You Manage Risks Strategically 

Critical security debt will continue to escalate unless organizations move away from outdated AppSec practices, particularly those established before AI-assisted coding and extensive third-party software usage became common. Continuing with outdated practices can lead to significant financial losses, damage to brand reputation, regulatory compliance penalties, and erosion of customer trust. 

Using tools like Veracode Risk Manager and Veracode Fix to prioritize security flaws and remediate critical flaws as quickly as possible is no longer a nice-to-have, it is becoming a modern-day necessity. Such tools unlock developer productivity by automating mundane manual tasks such as writing multiple patches to fix the same pattern of flaws. As a result, security teams can now focus on more strategic security aspects. 

Book a personalized demo to learn how Veracode can reduce your critical security debt.