6 Best Practices for Application Risk Assessments

For years, the annual penetration test or quarterly security scan served as the cornerstone of application risk assessments and application risk management. Teams would run the assessment, triage the findings, hand the report to developers, and wait for the next cycle. It felt like progress. It wasn’t.

Today, that model is not just outdated; it is actively dangerous. The threat landscape has been permanently altered by AI-driven vulnerability discovery, expanding software supply chains, and the relentless accumulation of security debt. We’re seeing years of latent technical debt now being surfaced in months. The compression of attack timelines means the window between “vulnerability exists” and “vulnerability is weaponized” has collapsed to a degree that periodic, point-in-time assessments simply cannot keep pace with.

For CISOs and security leaders, the mandate is clear: application risk assessments must evolve from scheduled events into a continuous, intelligence-driven discipline embedded in the fabric of how software is built, deployed, and governed. Here is what best-in-class looks like in 2026 and beyond.

Why Traditional Application Risk Assessments Fall Short

The fundamental problem with legacy assessment approaches is that they measure a snapshot of a system that never stops changing. Research shows that 79% of third-party libraries included in codebases are never updated after initial inclusion — meaning the software your teams built last year is growing less secure every day, with or without anyone touching the code.

Compounding this is the AI-generated code problem. Veracode’s testing of over 150 large language models reveals a stark reality: only 55% of AI-generated code passes basic security tests. Your developers are shipping faster than ever, but nearly half of the code coming out of AI-assisted workflows could contain known security vulnerabilities when explicit security guidance is absent. The gap between “code that works” and “code that works securely” is not closing — it is widening.

Periodic application risk assessments cannot address this volume, velocity, or variety. What CISOs need is a strategic framework built on six core best practices.

Best Practice 1: Make Application Risk Assessments Continuous, Not Periodic

The most consequential software security shift a CISO can lead is the move from point-in-time application risk assessments to continuous assessment integrated across the software development lifecycle (SDLC). Strategic risk management is about anticipating and preparing for potential risks rather than simply reacting to them. What is secure today will not necessarily be secure tomorrow.

This means embedding security testing — static analysis, dynamic analysis, software composition analysis — directly into CI/CD pipelines so that every code commit, every dependency update, and every container build triggers an automated risk signal. Findings surface in real time, at the moment they are introduced, rather than weeks later in a PDF report. Continuous feedback loops allow development teams to course-correct immediately, dramatically reducing the cost and effort of remediation while shrinking the window of exposure.

Fragmented security tooling is one of the most consistent inhibitors of effective application risk assessments. When SAST, DAST, SCA, container security, and risk management operate as disconnected point solutions, the result is siloed data, duplicated effort, and a distorted picture of actual organizational risk. Security teams spend more time correlating outputs than acting on them.

A consolidated, platform-based approach delivers continuous visibility, intelligent prioritization, and accelerated remediation across the entire portfolio — enabling DevSecOps teams to build fast and build secure simultaneously.

The goal is not to run more scans; it is to make assessment a living, automatic process that keeps pace with the speed of development.

Best Practice 2: Assess Risk at the Organization Level, Not the Application Level

Most security teams are conditioned to think in terms of individual findings: critical CVEs, vulnerable components, exposed endpoints. This instinct is understandable but strategically insufficient. The future of application risk assessments lies in understanding risk from the organizational lens — what impacts the business across the entire portfolio, not just within individual segments.

A critical vulnerability in an internal testing application is not the same risk as that same vulnerability in a customer-facing payments service. Application risk assessments must be contextualized by business impact: the data the application handles, the regulatory environment it operates in, the users it serves, and the dependencies that flow through it.

Rather than treating all findings equally, CISOs should insist on intelligent prioritization that answers the question Brian Roche, CEO of Veracode, articulated as the defining challenge of the AI era: “What risk actually matters?”. This means moving beyond raw vulnerability counts to a portfolio-wide view that surfaces which risks are exploitable, reachable, material, and consequential to the organization. A single, panoramic risk picture — from code to cloud — replaces siloed data and eliminates the noise.

Best Practice 3: Shift Left and Embed Security Earlier in the SDLC

Fixing a vulnerability after software ships costs significantly more — in time, effort, and organizational disruption — than catching it at the design or development phase. Yet many organizations still treat security assessment as a gate at the end of the development process. This approach is no longer viable.

Best-practice application risk assessments require embedding security earlier in the SDLC: threat modeling during planning, static analysis during coding, automated composition scanning during build, blocking malicious packages before they enter the code base, and dynamic analysis during testing. Security must be built in, not bolted on. The shift from a Software Development Lifecycle (SDLC) to a Secure Software Development Lifecycle (SSDLC) is not a technical upgrade — it is a cultural and strategic one that has to be led from the top.

Security leaders who have successfully made this shift report that developer friction decreases over time as security tooling integrates seamlessly into the environments developers already work in. When assessments produce actionable, contextual findings within the IDE rather than a spreadsheet at sprint’s end, developers engage with security differently — and the entire organization’s risk posture improves accordingly.

Best Practice 4: Extend Assessments Across the Software Supply Chain

Modern applications are not monolithic products; they are compositions of first-party code, open-source libraries, third-party services, containers, and increasingly, components generated or modified by AI models. A mature application risk assessment program cannot stop at the code your team wrote. It must extend across the entire software supply chain.

The attack surface is expanding exponentially. More code streams, more frequent releases, more AI-generated components, and wider technology diversity mean that traditional, perimeter-focused security models are structurally insufficient. High-profile supply chain compromises have demonstrated that a single vulnerable component, overlooked library, or untrusted dependency can cascade across many products and customers — eroding trust, inviting regulatory penalty, and disrupting operations.

Mature application risk assessments include continuous software composition analysis (SCA) to identify vulnerable open-source components, container security scanning to assess deployed environments, and Software Bill of Materials (SBOM) practices that provide persistent visibility into what is actually running in production. Proactive external attack surface management adds the outer layer — continuously surfacing exposure before attackers find it.

Best Practice 5: Govern AI-Generated Code as a First-Class Risk Domain

The rise of AI-assisted development has introduced a risk dimension that most application risk assessment frameworks were not designed to handle. AI models generate code at remarkable speed and with near-perfect syntactic correctness — but security accuracy remains stagnant at roughly 55%, a number that has barely moved across two years of breakthrough model releases from the industry’s leading AI providers.

This is not a temporary problem waiting for the next model release to solve it. It is a structural challenge that requires governance. CISOs must ensure that AI-generated code is subject to the same — or stricter — application risk assessment standards as human-written code, and that clear controls exist over how AI participates in the software lifecycle: what it generates, what recommendations it makes, and how its outputs are validated before reaching production.

AI can also be a powerful ally in remediation. Intelligent fix automation trained on credible security research can address the majority of common vulnerability classes without requiring developers to write a single line of new code. The key is that AI in the development process must be governed intentionally — not adopted uncritically because it accelerates shipping velocity.

Best Practice 6: Build Toward Demonstrable Software Trust

The question that application risk assessments have historically tried to answer is: “Did we scan it?” In the AI era, that question is no longer sufficient. The question boards, customers, and regulators are increasingly asking is: “Can you prove it can be trusted?”

Software Trust means an organization can continuously answer four critical questions:

• What risk actually matters?
• Can we reduce that risk fast enough?
• Can we govern AI safely?
• And can we prove software is safe to ship?

The organizations that can answer all four — with evidence, not assertions — are the ones that will earn and maintain the confidence of every stakeholder in an era of machine-speed threats.

This is the north star for CISO-led application risk management: moving from episodic testing toward a model that continuously identifies meaningful risk, drives remediation at scale, governs AI-assisted development, and creates the evidence base that software is trustworthy before it reaches production. Detection alone is not enough. Evidence is what matters.

Turn Application Risk Assessment Data into Board-Level Governance

Application risk assessments produce a great deal of data. Most of it never reaches the board in a useful form. This is a governance failure that CISOs have both the responsibility and the opportunity to fix — especially in the post-SEC Cyber Rules environment.

The SEC’s 2023 cybersecurity disclosure requirements direct public companies to describe their board’s oversight of cybersecurity risk and management’s role in assessing and managing material risks. This means that application risk assessment data is no longer just an operational input — it is a governance asset. Boards need to understand not just that assessments are happening, but what they reveal, how risk is being prioritized, and how the organization is trending over time.

CISOs who establish reporting frameworks that translate assessment data into board-ready risk language — using quantitative goals, benchmarking against peers, and tracking measurable progress — are building the transparency and accountability structures that the SEC’s rules were designed to incentivize. Transparency triggers the walk toward accountability, and accountability is the path to maturity.

Using a unified security platform with robust analytics and on-demand reporting allows security leaders to establish a baseline, identify areas for improvement, set quantitative goals, and track progress — giving the board the coherent, time-bound view of risk it needs to exercise meaningful oversight.

The Stakes Have Never Been Higher for Application Risk Assessments

The organizations that emerge from the current AI-driven security reckoning as trust leaders will not be the ones that ran the most scans. They will be the ones that transformed application risk assessments from a compliance exercise into a continuous strategic capability — embedded in development, contextualized to business impact, governed across the supply chain, and demonstrable to every stakeholder who needs confidence in the software they ship.

The vulnpocalypse is not a distant scenario. Decades of latent security debt are being surfaced in months. The window to act is open. The question is whether your application risk assessment program is designed for the world as it is — or the world as it was.

Veracode’s comprehensive application security platform helps organizations move from scanning to proving — delivering continuous visibility, intelligent prioritization, and demonstrable Software Trust across every line of code, every dependency, and every AI-assisted change. Contact Veracode to learn how we can help you build the application risk assessment program your organization needs.