Application Security Vulnerabilities to Watch out for in 2026

The threat model for software applications is changing so quickly. Organizations are dealing with a perfect storm of conditions: AI-authored code causing security vulnerabilities, supply chain attacks happening twice as often, and bad actors weaponizing exploits soon after they’re disclosed. Broken access control was 100% prevalent in applications assessed in the OWASP Top 10 2025 release, whereas security misconfiguration moved to second place, affecting 3% of all tested applications. In the first half of 2025, the National Vulnerability Database coordinated over 23,667 CVEs; an increase of 16% from a year prior.

The landscape is shifting dramatically. According to Cycode’s 2026 State of Product Security in the AI Era report, over 92% of organizations are actively using or piloting AI coding assistants, yet AI-generated code has become the #1 blindspot for application security teams. With 81% of organizations lacking full visibility into how AI is used across their software development lifecycle, understanding these emerging vulnerabilities is no longer optional, it’s critical for survival.

What Are Application Security Vulnerabilities?

Application security vulnerabilities are flaws in software code, architecture, or system configuration that attackers can abuse to gain unauthorized access to data, systems, or operations. There are vulnerabilities present in various aspects of the software development process, from initial design to implementation and then operational maintenance.

These vulnerabilities range from basic poor coding practices to fundamental design failings. They may be present in proprietary code, third-party dependencies, infrastructure code, or even the way we authenticate. The impact ranges from disclosure of information to exploitation of the system.

Top 11 Application Security Threats for 2026

The vulnerabilities below are the most significant enterprise application security threats to watch for in 2026. Each of these has withstood years of testing across millions of applications and against real-world attack data. By prioritizing these threats, you can allocate security resources accordingly and minimize the attack surface of your organization.

1. Broken Access Control

Broken access control is when applications do not restrict access to data or functionality that users should not be able to use. Attackers can use these vulnerabilities to view sensitive information or to make changes to data they shouldn’t have access to, and they can even execute administrative functions without credentials or access controls. The vulnerability includes insecure direct object references (IDOR), missing authorization checks, and privilege escalation issues.

This remains the number one application security risk for a critical reason: it’s both prevalent and devastating. OWASP data shows that 100% of applications tested contained some form of access control weakness, with 40 different Common Weakness Enumerations mapped to this category. A recent penetration testing report found broken access control accounted for 32% of all high-severity findings, representing a 40% surge from previous years.

How Broken Access Control Impacts Enterprises:

• Compliance violations and regulatory penalties due to unauthorized access to data
• Lateral movement within systems enables broader compromise
• Financial fraud through unauthorized transaction manipulation
• Reputational damage from data breaches and customer information exposure

2. Security Misconfiguration

Security misconfiguration refers to the improper deployment of systems, frameworks, or applications with insecure default settings, incomplete setups, or unnecessary features activated. These misconfigurations are often easily exploited, such as exposed debugging interfaces, default credentials still active on applications, over-permissive cloud storage buckets, and libraries that produce verbose error messages that can leak internal system information.

The move to cloud infrastructure and containerized applications has dramatically changed the configuration management landscape and added complexity to the challenge. Every new service, API, or microservice has dozens of configuration options to secure. This vulnerability has taken a huge leap from fifth place to second in the OWASP rankings, with 90% of applications demonstrating some form of misconfiguration during testing.

How Security Misconfiguration Impacts Enterprises:

• Cloud storage breaches exposing millions of customer records
• Containerized applications running with excessive privileges
• Development and staging environments exposed to internet access
• Unpatched systems due to poor asset inventory and configuration tracking

3. Software Supply Chain Compromise

Software supply chain compromise is an attack type that targets the development, build, or distribution process vs. the finalized application. Attackers plug in malicious code into dependencies, break build systems, or tamper with update mechanisms and push backdoored software to thousands of downstream users. These attacks take advantage of the established trust between the vendor and customers, and as a result, are extremely hard to spot.

In 2025, supply chain attacks doubled, occurring at a rate of 26 incidents per month based on recent data. $1.5B was taken in the 2025 Bybit theft, a wallet software supply chain attack, and then there was GlassWorm as a malicious group that scavenged VS Code extensions that auto-updated onto developer machines.

How Software Supply Chain Compromise Impacts Enterprises:

• Compromised third-party libraries affecting hundreds of internal applications
• Build pipeline attacks, injecting backdoors into proprietary software
• Leaked developer credentials are increasing by 12% year-over-year
• Extended breach detection times as malicious code hides in trusted sources

4. Cryptographic Failures

Cryptographic failures occur when applications fail to properly protect sensitive data with encryption, hashing, or other cryptographic controls. This includes using weak algorithms, exposing keys, sending data in plaintext, or not encrypting data at rest. These are weaknesses that attackers take advantage of to intercept communications, decrypt data at rest, or forge authentication tokens.

A single failure in cryptography could, for example, compromise years of sensitive information. Managing key rotation, certificate management, and making sure encryption is applied consistently on every possible data flow is a challenge for organizations.

How Cryptographic Failures Impact Enterprises:

• Customer payment information exposed through weak encryption
• Stolen authentication tokens enabling account takeovers
• Regulatory violations under GDPR, PCI DSS, and HIPAA
• Man-in-the-middle attacks on unencrypted API communications

5. Injection Threats

Attackers inject malicious code or commands into application inputs that the system executes. SQL injection is associated with the database, command injection with operating system calls, and cross-site scripting(XSS) with the web browser. Such attacks are successful because applications do not properly validate, sanitize, or escape user-supplied data before processing it.

Injection remains one of the most common vulnerability categories, even though it is detected so frequently. According to OWASP’s reviews, 38 distinct CWEs map to injection vulnerabilities, and the category possesses the second-most-frequently related CVEs. In the first half of 2025, 38% of all weaknesses were attributed to cross-site scripting and SQL injection.

How Injection Threats Impact Enterprises:

• Database compromise through SQL injection, extracting entire customer tables
• Remote code execution via command injection, taking over servers
• Session hijacking and credential theft through XSS attacks
• API manipulation leading to unauthorized transactions or data modification

6. Insecure Design

Insecure design is a class of design flaws embedded in the application architecture and threat model, not an implementation bug. These are by-design vulnerabilities, like using security questions for password recovery, not limiting the rate at which one can request specific workflows, or workflows that fail to consider abuse scenarios. Even a perfectly implemented program cannot redeem poorly designed software with inherent vulnerabilities.

This category highlights the need to move security to the left in the development lifecycle. In 2021, OWASP added insecure design to its list, which emphasizes that even well-implemented code will be vulnerable if the design is flawed. Teams must consider threat modeling, secure design patterns, and risk-based analysis of code before it is written.

How Insecure Design Impacts Enterprises:

• Business logic flaws enabling price manipulation in e-commerce systems
• Workflow bypasses that circumvent approval processes
• Resource exhaustion from a lack of rate limiting or throttling
• Privacy violations from insufficient data minimization in system design

7. Authentication Weaknesses

Authentication weaknesses occur when applications fail to properly verify user identities across multiple requests and/or fail to maintain session integrity for a user after authentication. Weak password policies, absent multi-factor authentication, predictable session tokens, and insufficient session timeout handling all fall into this category. 

These vulnerabilities can then be exploited by attackers using credential stuffing, brute force, or session hijacking methods to gain access to accounts. In June 2024, the Levi Strauss breach highlighted how weak authentication controls can expose businesses to the risk of credential stuffing, with numerous customer accounts compromised in this way.

How Authentication Weaknesses Impact Enterprises:

• Account takeovers leading to financial fraud and data theft
• Credential stuffing attacks leveraging billions of leaked passwords
• Insufficient multi-factor authentication adoption leaves accounts vulnerable
• Session fixation attacks maintain access even after logout

8. Integrity Failures

Integrity failures occur when applications cannot verify the legitimacy and integrity of updates to code, data, or infrastructure. This includes accepting unsigned software patches, not validating serialized objects, or trusting data from sources without any source verification. Attackers take advantage of these vulnerabilities to either inject malicious code within the trusted update channels or to manipulate the serialized data to achieve remote code execution.

Organizations extensively use CI/CD pipelines and auto-update mechanisms to deploy quickly, and this vulnerability addresses such scenarios. Also, without proper signing, attestation, and verification processes, attackers can exploit software during the build or distribution stage.

How Integrity Failures Impact Enterprises:

• Malicious updates deployed to production systems
• Deserialization attacks leading to remote code execution
• Compromised artifacts in build pipelines affecting all deployments
• Third-party plugin modifications introducing backdoors

9. Logging and Alerting Gaps

Logging and alerting gaps occur when an application does not produce, retain, or monitor events that are meaningful with respect to a security event. Insufficient logging can hinder security teams from detecting active attacks, performing post-mortems, or meeting compliance requirements. Common shortcomings include no security events, logs not integrity-protected, and a rudimentary level of central monitoring.

Without proper logs, minor incidents can escalate into major breaches. Compromised organizations without full logging capabilities take an average of 98 more days to discover what happened than those with an infrastructural monitoring deployment. This category moved from tenth place in 2021 to ninth place in 2025, as companies finally understood how fundamental it is to incident response operations.

How Logging and Alerting Gaps Impact Enterprises:

• Delayed breach detection allows attackers months of access
• Compliance violations under regulations requiring audit trails
• Inability to detect insider threats or privilege abuse

10. Mishandling Exceptional Conditions

Mishandling exceptional cases, added in 2025, occurs when applications do not handle errors and exceptions correctly. Upon unexpected inputs or rare system conditions, applications might reveal sensitive information through error messages, fall into insecure modes of operation, or expose timing side channels. This category includes 24 CWEs that have logical errors and/or exceptional conditions.

Race conditions are a particularly nasty example of this, and some novel exploit techniques have been introduced recently. The vulnerabilities described are time-dependent and allow an attacker to jump past the authentication checks, change financial transactions between any two users of the system, including administrators.

How Mishandling Exceptional Conditions Impacts Enterprises:

• Verbose error messages leaking the database structure and credentials
• Race conditions enabling double-spending in payment systems
• Fail-open scenarios granting access when systems encounter errors
• Time-of-check to time-of-use vulnerabilities in authorization

11. AI/ML Pipeline Exploitation

AI/ML pipeline exploitation targets vulnerabilities in machine learning systems, including everything from poisoning training data to adversarial attacks against deployed models. This includes poisoning model training datasets, creating adversarial inputs to manipulate model outputs, or conducting model extraction to steal private information from the model. 

The rapid adoption of AI without the necessary security controls has created a new attack surface, creating new AI-specific code vulnerabilities. AI models generate insecure code almost half the time (45%) without explicit mention of security, research has shown. Autonomous AI agents create new attack surfaces such as identity impersonation, unauthorized agent actions, and even mistaken agent decision-making.

How AI/ML Pipeline Exploitation Impacts Enterprises:

• Data poisoning attacks corrupting model behavior through contaminated training data
• Model extraction enables competitors to steal proprietary AI intellectual property
• Prompt injection attacks, manipulating AI outputs to leak confidential information
• Adversarial inputs causing autonomous systems to make dangerous decisions

Why App Security Issues Are on the Rise

The rise in application security vulnerabilities comes from fundamental shifts in software development, deployment, and operation. The modern tech landscape has organizations in a challenging paradox, where they need to ship features faster than ever while also expanding their solutions with new technology resources, all of which are potential attack surfaces. Here are the five main enablers propelling the rise of app security problems.

AI-Generated Code Is Becoming a Primary Source of New Vulnerabilities

While artificial intelligence-coding assistants have transformed developer productivity, they have also introduced vulnerabilities at an unprecedented scale. An analysis of more than 100 LLMs across 80 coding tasks found definitive proof that 40% of AI-generated applications contain vulnerabilities and 62% of code generated by the latest LLMs contained at least one exploitable vulnerability (e.g., SQL injection or buffer overflows), according to Cycode’s state of Product Security in the AI Era report.  The problem extends beyond sheer volume to the types of vulnerabilities introduced. AI models generate code that is vulnerable to cross-site scripting 86% of the time and to log injection 88% of the time.

The root cause lies in how AI models learn. Large language models train on vast repositories of open-source code, absorbing both good and bad patterns. Because insecure coding practices appear frequently in training data, AI assistants replicate them. A recent study found that 62% of AI-generated solutions contain design flaws or known vulnerabilities, even when using the latest foundation models. AI code vulnerabilities are particularly insidious because the code appears functional and often passes basic tests, leading developers to integrate it without a thorough security review.

Key risks from AI-generated code include:

• Cross-site scripting vulnerabilities in 86% of generated web application code
• Missing input validation and sanitization in API endpoints
• Insecure cryptographic implementations in 14% of cases
• Lack of proper authorization checks in access control logic

Agentic Automation Is Creating More Complex and Unpredictable Attack Paths

AI agents operating independently, without human intervention, are exploiting attack vectors that legacy security tools can’t detect. These agents make decisions, read data, run code without human control, and open new venues of exploitation. When an agent’s decision-making is undermined, attackers receive a force multiplier element that can continue to wreak havoc in your environment.

The complication arises from how agents interact with systems. Compared to the traditional applications, which are predictable in nature, agents adjust their functioning according to environmental constraints and goals. This makes it nearly impossible to predetermine for all possible execution paths or security boundaries.

Attack scenarios enabled by agentic automation:

• Identity spoofing, where agents impersonate legitimate users or systems
• Privilege escalation through multi-step automated exploitation chains
• Data exfiltration by agents that navigate through multiple security controls
• Resource manipulation as agents discover and exploit unmonitored APIs
• Cascading failures occur when compromised agents trigger actions across interconnected systems

Offensive AI Is Accelerating the Speed and Scale of Vulnerability Discovery

Attackers are turning artificial intelligence into an offensive weapon to find vulnerabilities more quickly than defenders can patch them. Research reveals 161 CVEs were exploited in the first half of 2025, with almost half associated with malware or ransomware campaigns. The time between vulnerability disclosure and actual exploitation has been reduced to hours, with proofs of concept released within 48 hours of a critical vulnerability being announced.

The scanning tools powered by AI can go through billions of possible code paths and pinpoint any vulnerable patterns quickly. This represents a sea change in the economics of cybercrime, as lower-skilled attackers are now able to launch more advanced attacks. According to ENISA, the EU agency for cybersecurity, in 2025, AI-powered phishing attacks accounted for over 80% of observed social engineering attacks globally.

How offensive AI changes the threat landscape:

• Automated vulnerability scanning, analyzing entire codebases in minutes
• AI-generated exploit code reducing time-to-weaponization by 90%
• Deepfake phishing is a growing concern, with 62% of organizations experiencing such attacks
• Polymorphic malware that rewrites itself to evade detection
• Scale attacks where single attackers can target thousands of systems simultaneously

ML and Data Pipelines Are Becoming High-Value Targets as AI Adoption Surges

Machine learning pipelines operate at an immense scale with sensitive data, making them targets for adversaries seeking to extract or corrupt model behavior. These pipelines consume training data from different sources, undergo complex transformations, and produce models that are business-critical. Pipeline security has become a boardroom concern as organizations realize the business impact of corrupted AI systems.

The problem extends beyond technical controls to basic visibility gaps. Many organizations lack comprehensive inventories of AI models, training datasets, and deployment environments. This blind spot opens a path for attackers to subvert ML systems without being detected. The majority of ML models never make it to production because MLOps practices are fragmented, creating security vulnerabilities that attackers can exploit.

Critical vulnerabilities in ML and data pipelines:

• Training data poisoning corrupts model behavior at the source
• Model extraction attacks, stealing proprietary algorithms worth millions
• Inference API exploitation reveals sensitive information through queries
• Pipeline access control failures enabling unauthorized model manipulation
• Lack of model versioning and audit trails prevents forensic investigation

Shadow AI Usage Is Growing Faster Than Security Teams Can Govern It

Employees across organizations are trying out AI tools without IT’s blessing or security’s oversight, opening gaps in enterprise security defenses. Every unapproved shadow AI tool is a new gap through which data can leak, compliance standards can be violated, or an attack can be launched.

The problem is accelerating. A typical organization now uploads 8.2 GB of data to AI apps each month, compared with 5 GB in September 2024. The explosion is outstripping the capability of security teams to put in place governance models, implement monitoring, and educate users about risks.

Risks from ungoverned shadow AI:

• Proprietary code and trade secrets pasted into public AI chatbots
• Customer data uploaded to unvetted AI services for analysis
• Compliance violations as AI tools process regulated information
• Model training on confidential corporate data shared through prompts
• AI-generated decisions are impacting business without audit trails or accountability

How to Protect Your Enterprise from Modern Application Security Risks

Defense against today’s application security threats requires addressing both people and technology. Companies need to embed security in the software at every stage of development, while maintaining agility as a competitive differentiator. Below are several approaches to establishing a strong baseline for managing the risks across your portfolio of applications.

Enforce Secure Coding Standards: Implement and enforce organization-wide secure coding principles that cover standard vulnerability patterns as outlined by the OWASP Top 10 or others. Provide developers with automated security checks at development rather than deployment. Ongoing training helps teams understand not just how vulnerabilities work, but also how to avoid them in the languages and frameworks they’re using every day.

Integrate Continuous Security Testing: Add security scanning at every layer of the development pipeline, starting with IDE plugins that capture issues during coding, through pre-commit hooks, CI/CD integration, and production monitoring. Use static application security testing (SAST), software composition analysis (SCA), and dynamic testing (DAST) to detect different types of vulnerabilities. It gives real-time feedback to developers before they lose the context and reduces fix time from weeks to hours.

Strengthen Security Controls: Deploy defense-in-depth with multiple layers of security controls such as WAF (Web Application Firewalls), API gateways, secrets management, identity and access management solutions. Use the principle of least privilege on systems, services, and users everywhere to contain blast radius. Implement zero-trust based systems that verify every user request regardless of how and where it originates.

Improve Visibility: Invest in application security posture management (ASPM) tools that can give you a cohesive view into your application portfolio, dependencies, and infrastructure as a whole. Maintain software bills of materials (SBOMs) for all applications to track and trace components and dependencies. Dashboards displaying security posture, trends, and risk concentration provide real-time data for decision-making and resource allocation.

Adopt Automated Remediation: Use AI-powered solutions that not only spot vulnerabilities but offer context-specific remediation advice, or even immediately apply patches. Cycode’s AI Exploitability Agent exemplifies this approach by prioritizing vulnerabilities based on actual exploitability rather than relying solely on CVSS scores, helping teams fix what truly matters 99% faster.

Best Practices for Reducing Software Security Problems

Moving beyond reactive vulnerability management requires adopting proactive practices that prevent security issues from entering codebases in the first place. These best practices represent proven approaches that leading organizations use to systematically reduce application security risk while maintaining development velocity.

1. Build a Threat Modeling Discipline Into Every Major Release

Threat modeling identifies security problems in the design phase, where they’re least expensive to resolve. By systematically considering how an attacker would compromise your application, teams can develop countermeasures before writing any code. This process brings security left as early as possible in the development cycle, where architectural changes are still practical.

Good threat models require structured processes, not ad-hoc brainstorming. Frameworks such as STRIDE or PASTA should be used to systematically list threats, rate their likelihood and impact on the organization, and detail mitigations. For cloud-native software, expand the threat model to consider container orchestration, service mesh traffic, and serverless function calls.

Critical threat modeling practices:

• Conduct threat modeling workshops during sprint planning for major features
• Document trust boundaries, data flows, and privilege levels in architecture diagrams
• Identify and prioritize threats based on business impact and likelihood
• Define security requirements and acceptance criteria derived from threat analysis
• Review and update threat models when application architecture changes

2. Adopt Dependency Hygiene and Maintain a Verified Software Bill of Materials

Modern applications have hundreds or even thousands of third-party dependencies, each of which may pose a supply chain risk. A comprehensive software bill of materials (SBOM) allows companies to identify the most commonly used libraries, enabling them to react quickly when vulnerabilities are found.

However, dependency hygiene is more than just an inventory of components; it is the active management of its security posture. This encompasses automatic updates for security patches in dependencies, clearing of orphaned libraries that expand the attack surface, and verifying critical dependency provenance through cryptographic signatures.

Best practices for dependency management:

• Generate and maintain SBOMs for all applications using standardized formats (SPDX, CycloneDX)
• Automatically scan dependencies for known vulnerabilities in CI/CD pipelines
• Implement policies requiring signed builds and attestations for critical components
• Monitor for compromised packages in dependency repositories you consume
• Establish processes for emergency patching when critical vulnerabilities emerge

3. Centralize Risk Prioritization to Focus on High-Impact Weaknesses

Not all vulnerabilities deserve equal attention. Given resource constraints, organizations that try to fix everything based on the CVSS score always end up wasting their efforts on low-risk issues while critical exposures remain unfixed. By integrating business context, exploitability data, and production exposure, centralized security risk prioritization helps teams focus on what really counts.

For example, a SQL injection in an internet-facing customer portal is far more urgent than the same type of vulnerability in an internal tool with limited access. More thoughtful prioritization comes from automated systems that take CISA’s Known Exploited Vulnerabilities catalog, the availability of proof-of-concept, and ongoing exploitation attempts into account.

Effective risk prioritization strategies:

• Correlate CVSS scores with reachability analysis to identify exploitable code paths
• Prioritize vulnerabilities in internet-facing assets and those processing sensitive data
• Factor in compensating controls like WAF rules when assessing actual risk
• Use threat intelligence feeds to identify vulnerabilities under active exploitation
• Create SLAs tied to risk levels rather than severity scores alone

4. Provide Ongoing Secure Coding Training for Developers

The level of developer security knowledge is directly proportional to both the number and severity of vulnerabilities introduced during the software development life cycle. Companies with thorough secure coding training can experience measurable decreases in vulnerability density. Training needs to be continuous, pragmatic, and should focus on the actual technology and frameworks developers use day-to-day. 

Effective training extends past annual compliance checkboxes to deliver just-in-time learning embedded into the developer’s day. When a developer generates a potentially exploitable SQL injection, instant feedback, along with resources for learning, goes much further than some generic training five months later.

Developer security training essentials:

• Language-specific secure coding courses (Python, Java, JavaScript, Go, etc.)
• Hands-on vulnerability labs where developers exploit and fix security issues
• Monthly security champions programs to build expertise within development teams
• Integration of security learning into onboarding for all new developers
• Regular updates covering emerging threats like AI code vulnerabilities

5. Standardize Change Management and Governance Across Pipelines

Inconsistent deployment practices and ad-hoc change management create security gaps that attackers exploit. With standardized governance, every code change is subject to the right type of security examination before it gets deployed into production. For example, code review must be conducted, automated security gates must be in the CI/CD pipeline, and separation of duties between development and deployment must be enforced.

Modern change management needs to find the balance between the rigor of security and the velocity of deployment. Organizations that ship code several times daily cannot manually review every change for security; at the same time, they cannot relax security standards. This can be done so that security validation is automated in deployment pipelines, while high-risk changes are ensured by human review. Cycode’s MCP Server helps streamline secure development workflows.

Pipeline governance best practices:

• Require pull request reviews with security checklist validation before merging
• Implement automated security gates that block deployments with critical findings
• Enforce signed commits and verified builds throughout the CI/CD pipeline
• Maintain immutable audit logs of all changes, approvals, and deployments
• Separate production deployment privileges from development access

Strengthen Your Defenses Against Security Threats to Software Applications with Cycode

Cycode provides the only comprehensive AI native application security platform on the market today, purpose-built for the age of AI. Organizations are dealing with fragmented tools, alert overload, and the inability to keep up with the modern development ecosystem today. With a unified platform that fuses best-of-class scanning with intelligent prioritization and risk-based automated remediation across your entire software development lifecycle, Cycode eliminates these challenges.

Cycode provides end-to-end coverage from first commit to production runtime, unlike point solutions that create visibility gaps and integration headaches. It correlates results from all security tools into a single view using the platform’s Risk Intelligence Graph, eliminating noise and presenting only those vulnerabilities that could pose a threat to your business. 

Cycode’s comprehensive capabilities include:

• Proprietary AST Scanners: Enterprise-grade SAST, SCA, IaC scanning, and container security with 31% faster scanning speeds and superior accuracy
• Complete ASPM Platform: Unified visibility across code, pipelines, and cloud with risk-based prioritization using exploitability and business context
• Software Supply Chain Security: Deep pipeline protection, secrets detection across ticketing and collaboration tools, and SBOM generation with continuous monitoring
• AI-Native Security: Purpose-built controls for AI-generated code, autonomous agent governance, and ML pipeline protection
• Automated Remediation: AI-powered fix suggestions that developers can implement with one click, dramatically reducing mean time to resolution
• Compliance Automation: Always-on attestation and evidence collection for SSDF, SOC2, ISO, DORA, PCI, and other frameworks
• Developer-First Experience: Security embedded directly into IDEs, pull requests, and existing workflows with no productivity tax
• ConnectorX Ecosystem: 100+ integrations with existing tools to unify findings or replace fragmented security stacks entirely

Book a demo today and see how Cycode helps enterprises proactively manage and reduce application security vulnerabilities.