Shadow SaaS: What Every CISO Needs To Know
What is Shadow SaaS
The “Shadow SaaS” problem refers to the unauthorized use of Software-as-a-Service (SaaS) applications within an organization without the knowledge or approval of the IT department or management. This typically occurs when employees use external cloud-based services to fulfill their work-related tasks without following proper protocols or security measures. Shadow SaaS can lead to security vulnerabilities, data breaches, compliance issues, and loss of control over sensitive company information.
Why Shadow SaaS Problem is Growing
- Ease of Access: SaaS applications are often readily accessible and can be easily procured by individuals or teams within an organization without the need for IT involvement. This ease of access encourages employees to seek out solutions independently, bypassing official channels.
- Flexibility and Convenience: Many SaaS applications offer flexibility and convenience, allowing users to quickly address their needs without waiting for IT approval or assistance. This convenience can cause employees to adopt unauthorized solutions.
- Consumerization of IT: The consumerization of IT means that employees are increasingly accustomed to using user-friendly and feature-rich applications in their personal lives. They may expect similar experiences in the workplace and turn to shadow SaaS when official tools do not meet their expectations.
- Remote Work and BYOD: The rise of remote work and Bring Your Own Device (BYOD) policies has further fueled the adoption of shadow SaaS. Employees working from home may seek out their preferred tools to facilitate collaboration and productivity, even if those tools are not officially sanctioned by the organization.
- Lack of Awareness: In some cases, employees may not be fully aware of the security risks associated with shadow SaaS usage. They may not realize the importance of adhering to IT policies or may underestimate the potential consequences of using unauthorized applications.
- New Gen AI Apps: The rapid pace of innovation in generative AI technology has resulted in a proliferation of new SaaS solutions catering to various business needs. Employees may be enticed by the latest tools and services that help them boost their productivity,, leading them to experiment with shadow SaaS without considering the implications for security and compliance.
A combination of these factors contributes to the increasing prevalence of shadow SaaS within enterprises, posing challenges for CISOs and IT departments striving to maintain control and security over the organization’s digital assets.
Why are Gen AI SaaS Apps So Risky?
Among the different categories of SaaS applications, we think that shadow SaaS related to Gen AI is riskier. Here is why:
- Driven by Data: Gen AI apps require enterprise data in many cases to summarize, build chatbots, learn and produce output that the user wants.
- Helps with Productivity boost: Since many apps help users in their day to day tasks, users are likely to sign up with them just to get higher productivity and avoid being tracked. So the users may not go through SSO or even use their personal card to sign up. No wonder ChatGPT had tens of millions of people sign up within first few months and many organizations were caught off-guard with its use with their employees and data
- Non-SSO access: Most companies offering these apps avoid SSO integration as it is both expensive and time consuming. They want to make it easier for users to sign up and start using the app. Some in fact simply allow you to login with your email and a passcode or magic link sent over email.
What Shadow SaaS is Such a Big Problem?
Shadow SaaS can lead to data leaks, compliance issues and even copyright infringement or IP issues. Recently the SEC has also mandated the enterprises to have visibility around their SaaS app usage and integrations and to disclose that. See the hackernews article for more details.
Let’s look at the risk of Shadow SaaS in these areas.
Shadow SaaS == Data Leaks
The reasons above have caused increased use of shadow SaaS in enterprises. SaaS apps by design hold data with them and allow access to that data using APIs and web or mobile apps. So shadow SaaS also means invisible data activity. Here are some ways data leaks can happen:
- Lack of Visibility: When employees use unauthorized SaaS applications, IT departments may lack visibility into the data being stored and shared within these applications. This lack of visibility makes it difficult to monitor and protect sensitive information effectively.
- Inadequate Security Controls: Shadow SaaS applications may not adhere to the organization’s security policies and standards. They may lack essential security features such as encryption, access controls, or multi-factor authentication, increasing the risk of unauthorized access and data breaches.
- Data Silos: Shadow SaaS applications create data silos, where critical information is dispersed across various platforms that are not integrated with the organization’s centralized systems. This fragmentation makes it challenging to manage and secure data consistently across the enterprise.
- Data Sharing Risks: Employees may unwittingly expose sensitive data by sharing files or collaborating within unauthorized SaaS applications. Without proper controls in place, sensitive information may be shared with external parties or accessed by unauthorized individuals, increasing the likelihood of data leaks.
- Account Compromise: Shadow SaaS accounts are often managed independently by individual employees, making them more susceptible to security threats such as phishing attacks or password breaches. If an employee’s account is compromised, attackers may gain access to sensitive data stored within the unauthorized application.
This lack of visibility, inadequate security controls, data silos, sharing risks, and account compromises associated with shadow SaaS usage can collectively increase the likelihood of critical data leaks within an organization.
Shadow SaaS == Compliance Risks
- Data Privacy Regulations: Shadow SaaS applications may not comply with data privacy regulations such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States. These regulations require organizations to implement specific measures to protect the privacy of personal data, including obtaining consent for data processing, ensuring data security, and providing individuals with rights over their data. Unauthorized use of SaaS applications may result in the storage or processing of personal data without proper safeguards, leading to violations of data privacy regulations.
- Data Residency Requirements: Some industries and regions have regulations that mandate data residency requirements, specifying where data must be stored and processed. Shadow SaaS usage may result in data being stored in locations that do not comply with these requirements, leading to violations of regulatory obligations.
- Industry-Specific Standards: Certain industries, such as healthcare (HIPAA), finance (GLBA), or government (FISMA), have specific compliance standards and regulations that organizations must adhere to. Shadow SaaS usage may result in the storage or transmission of sensitive data in violation of these standards, leading to regulatory penalties and reputational damage.
- Security Standards: Many compliance frameworks require organizations to implement specific security controls to protect sensitive data from unauthorized access, disclosure, or modification. Shadow SaaS applications may lack adequate security controls, such as encryption, access controls, or audit trails, leading to non-compliance with security standards.
- Contractual Obligations: Organizations may have contractual agreements with customers, partners, or vendors that impose specific requirements regarding data security, confidentiality, or usage. The unauthorized use of SaaS applications may violate these contractual obligations, leading to legal disputes and financial liabilities.
- Internal Policies: Organizations often have internal policies and guidelines governing the use of IT resources, including SaaS applications. Shadow SaaS usage may contravene these policies, resulting in disciplinary actions or termination of employment for employees involved.
Overall, the use of shadow SaaS applications can introduce significant compliance risks for organizations, potentially leading to legal and regulatory consequences, financial penalties, and reputational harm. It is essential for organizations to implement robust governance and control measures to mitigate these risks effectively.
Shadow SaaS for Gen AI Apps == Copyright and IP Issues
The hidden use of the generative artificial intelligence ( Gen AI) applications can potentially lead to copyright and intellectual property (IP) issues in several ways:
- Unauthorized Use of Source Material: Generative AI applications, particularly those trained on existing datasets, may inadvertently generate content that resembles copyrighted material. If this content is used or distributed without permission from the original copyright holder, it could lead to allegations of copyright infringement. We have already seen the NYTimes pending lawsuit on OpenAI and Microsoft.
- Creation of Derivative Works: Generative AI tools can create new works based on existing content. While these works may be original in some respects, they may still incorporate elements of copyrighted material. Determining the extent to which these works infringe upon existing copyrights can be complex and may lead to legal disputes.
- Ownership of Generated Content: In cases where generative AI applications are used to create content, questions may arise regarding the ownership of the resulting works. While copyright law generally attributes authorship to human creators, the involvement of AI algorithms in the creative process can blur the lines of ownership and raise questions about who holds the rights to the generated content.
- Plagiarism and Attribution: Generative AI tools capable of producing text, images, or other creative works may inadvertently produce content that closely resembles existing works. If this content is presented as original without proper attribution to the original creators, it could be considered plagiarism and may result in accusations of intellectual property theft.
- Fair Use and Transformative Use: The use of generative AI applications to modify or transform existing copyrighted material raises questions about fair use and transformative use doctrines. While transformative works may be protected under copyright law, determining whether a particular use qualifies as transformative can be subjective and may require legal analysis.
- Liability for Infringement: Individuals or organizations that use generative AI applications to create or distribute copyrighted material without authorization may be held liable for copyright infringement. This can result in legal action, including demands for damages, injunctions to cease infringing activities, and potentially criminal charges in severe cases.
To mitigate these risks, enterprises and employees using generative AI applications should exercise caution when creating and sharing content, ensure compliance with copyright law and intellectual property rights, seek appropriate permissions when using copyrighted material, and consider consulting legal counsel for guidance on complex copyright issues. Additionally, developers of generative AI tools should implement safeguards to prevent or mitigate copyright infringement, such as incorporating content filters, providing clear guidelines for users, and fostering awareness of copyright laws and best practices.
There are several ways to mitigate these threats from SaaS Apps. We will write about those and how Kitecyber helps with this problem in a separate article.
If you would like to know more about how we help with these problems with an easy to install and manage solution, please reach out to info@kitecyber.com
