Frameworks, libraries, packages and APIs help teams accelerate delivery, reduce costs and focus on innovation.<\/p>\n\n
However, this agility presents an increasing challenge for organizations: how to ensure that these components do not introduce vulnerabilities, risks of compliance or intellectual property issues before the introduction of the application into production?<\/p>\n\n
Currently, around 80%<\/a> of application code comes from open source projects and is subject to different open source licenses, according to Black Duck. This means much of an application’s security and governance depends on components developed and maintained by third parties.<\/p>\n\n In this scenario, identifying vulnerabilities before the deployment is no longer just a good safety practice.<\/p>\n\n The growth of open source software use has brought numerous benefits to development, but has also increased the complexity of risk management. In practice, many companies do not have full visibility about:<\/p>\n\n The result is that applications often reach production containing vulnerable, outdated or inconsistent components with compliance requirements. In some cases, a single compromised library can expose critical systems, facilitate attacks, cause data leak or generate operational indisposability.<\/p>\n\n When speaking in open source security, many organizations focus only on known vulnerabilities. However, there is another equally relevant challenge: compliance with software licenses.<\/p>\n\n Any open source component is distributed under a license that determines how that software can be used, modified and redistributed.<\/p>\n\n The problem is that corporate environments can contain thousands of different components, making it virtually impossible to manually control the obligations associated with each license.<\/p>\n\n In practice, some organizations classify open source licenses according to the level of obligations they impose on use, modification and distribution.<\/p>\n\n They are considered to be at lower risk because they have few restrictions on the use and distribution of the software. Some examples include:<\/p>\n\n Also known as limited copyleft licenses, they require certain modifications made in the component to be made available under the same license. Some examples are:<\/p>\n\n They represent greater legal exposure and may require, depending on the model of use, the public availability of parts of the derived code. Some examples include:<\/p>\n\n Without an appropriate governance strategy, many companies discover problems related to licensing only during audits, due diligence processes, mergers, acquisitions or legal reviews.<\/p>\n\n Although the adoption of open source is virtually universal, many development, security and compliance teams face difficulties in controlling these risks.<\/p>\n\n Most companies do not have a centralized view of all the components used in their applications. Without an updated inventory, it is virtually impossible to assess safety risks or compliance.<\/p>\n\n Not all libraries are added directly by the developers.<\/p>\n\n Many dependencies reach the environment through other components, creating complex layers of software that are not always visible to teams.<\/p>\n\n A critical vulnerability may be present in the application without the team even knowing its existence.<\/p>\n\n Receiving thousands of security alerts does not necessarily mean being more protected.<\/p>\n\n Without an appropriate context, teams have difficulty identifying which vulnerabilities represent real risks to the business and which can be treated later.<\/p>\n\n In addition to the vulnerabilities, there is the challenge of monitoring the licenses associated with the components used.<\/p>\n\n The absence of this visibility can generate risks of intellectual property, compliance problems, non-conformity with corporate policies and legal exposure during audits.<\/p>\n\n Development Times are constantly charged by speed. When safety is analyzed only at the end of the development cycle, important problems are usually found too late, generating delays, rework and increased costs.<\/p>\n\n The adoption of AI tools for software development brought significant productivity gains.<\/p>\n\n Solutions such as GitHub Copyot, ChatGPT and other code assistants help developers generate pieces of software in seconds. Generator AI tools can be trained with large volumes of public code.<\/p>\n\n In specific situations, the suggestions generated may present correspondences with existing code, creating challenges related to traceability, copyright, licensing and governance.<\/p>\n\n In practice, a developer may incorporate a section suggested by AI without knowing its origin or associated licensing obligations. This creates a new layer of risk related to compliance, copyright, intellectual property and software governance.<\/p>\n\n As AI tools become part of the routine of development teams, controlling the origin and compliance of the code generated becomes a growing need.<\/p>\n\n The most efficient way to reduce risks is to integrate security and governance activities into the development cycle from the early stages.<\/p>\n\n The first step is to know exactly which components are part of the application.<\/p>\n\n This includes direct libraries, transitional dependencies, containers, frameworks and third party packages. Without visibility there is no effective risk management.<\/p>\n\n After identifying the components present in the application, it is necessary to compare them with public and private databases of vulnerabilities.<\/p>\n\n This allows quick discovery, critical vulnerabilities, obsolete components, libraries actively exploited by attackers and versions that need to be updated.<\/p>\n\n Not every vulnerability represents the same threat. An efficient analysis also considers severity, probability of exploitation, operational impact, application exposure and business criticism.<\/p>\n\n The sooner a problem is identified, the less your correction cost. Therefore, more mature organizations incorporate automatic security controls directly into the pipelines of development and continuous integration.<\/p>\n\n This approach reduces rework, accelerates delivery and improves the quality of the code released for production.<\/p>\n\nWhy do open source vulnerabilities pose a risk to companies?<\/strong><\/h2>\n\n
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Risk goes beyond vulnerabilities: governance of open source licenses<\/strong><\/h2>\n\n
Permitted licenses<\/strong><\/h2>\n\n
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Semi-permissive Licenses<\/strong><\/h2>\n\n
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Restrictive Licenses<\/strong><\/h2>\n\n
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The main challenges facing companies<\/strong><\/h2>\n\n
Lack of inventory of open source components<\/strong><\/h3>\n\n
Invisible transitional dependencies<\/strong><\/h3>\n\n
Difficulty in prioritizing corrections<\/strong><\/h3>\n\n
Lack of control over open source licenses<\/strong><\/h3>\n\n
Pressure to accelerate delivery<\/strong><\/h3>\n\n
The invisible challenge of generative AI in code creation<\/strong><\/h3>\n\n
How to identify vulnerabilities before entry into production<\/strong><\/h3>\n\n
Create a complete inventory of the components used<\/strong><\/h3>\n\n
Crossing components with known vulnerability bases<\/strong><\/h3>\n\n
Assessing the risk context<\/strong><\/h3>\n\n
Automate checks on the DevSecOps pipeline<\/strong><\/h3>\n\n
How to reduce risks in open source code?<\/strong><\/h2>\n\n