Addressing Shadow AI Risks with Zendata AI Governance
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Introduction

As businesses integrate artificial intelligence (AI) into their operations, Shadow AI has become a significant concern. Shadow AI refers to using AI tools and applications without formal oversight. This can lead to serious risks, including data breaches, compliance issues, operational inefficiencies and ethical dilemmas.

Managing these risks is essential for any organisation. Effective AI governance and strong security measures are vital to mitigating the potential downsides of Shadow AI. 

This article will discuss the business risks associated with Shadow AI and how Zendata provides comprehensive solutions to address these challenges, ensuring that businesses can safely and effectively harness the power of AI.

Understanding Shadow AI

Definition of Shadow AI

Shadow AI refers to using artificial intelligence applications, tools, and systems within an organisation without formal approval or oversight by the IT or data governance teams. These unauthorised AI implementations often occur because departments or individual employees seek to solve specific problems or gain insights quickly, bypassing standard protocols and controls.

Common Examples of Shadow AI in Businesses

Shadow AI can manifest in various ways within a business:

  • Third-party AI Tools: Employees might use third-party AI tools for data analysis, customer insights, or automating tasks without informing the IT department.
  • Custom AI Models: Teams might develop their own AI models using open-source frameworks, which are then deployed without proper security checks or integration into the company’s existing data infrastructure.
  • Cloud-based AI Services: Departments might subscribe to cloud-based AI services for specific projects, leading to potential data leakage or non-compliance with company policies.

Reasons for the Rise of Shadow AI in Organisations

Several factors contribute to the rise of Shadow AI:

  • Speed and Agility: Employees often turn to Shadow AI to quickly address pressing business needs without waiting for the slower, formal approval processes.
  • Accessibility of AI Tools: The increasing availability and ease of use of AI tools and platforms make it simple for non-technical staff to implement AI solutions independently.
  • Resource Constraints: Limited resources or budgets within IT departments can lead other departments to take initiative, using whatever tools they can access to meet their objectives.

Understanding Shadow AI is the first step towards recognising its risks and taking action to manage it effectively.

Business Risks Associated with Shadow AI

Several businesses, including companies like Samsung, have banned ChatGPT and other GenAI applications due to the risk of accidental (or intentional) data leakage. Back in 2023, Bloomberg reported that an internal Samsung survey found that 65% of respondents viewed GenAI as a security risk.

Data Security Risks

Shadow AI can lead to significant data security risks. When AI tools are used without proper oversight, there is a high chance of unauthorised access to sensitive data. According to a survey by LayerX, more than 6% of employees have pasted sensitive data into GenAI, directly putting their organisation at risk of data exfiltration.

This can result in data breaches, exposing the company to financial losses and reputational damage. The lack of centralised control makes it challenging to track and manage data flow, increasing the risk of data leaks.

Key issues include:

  • Unauthorised Data Access: Without IT oversight, employees may not follow best practices for data security, leading to potential breaches. Sensitive data could be accessed or transferred through insecure channels.
  • Lack of Encryption: Shadow AI tools may not have strong encryption, making data vulnerable during storage and transmission.
  • Inadequate Monitoring: Without centralised control, it is difficult to monitor and audit AI tool usage, increasing the risk of unnoticed breaches or data misuse.

Compliance Risks

Using AI tools without formal approval can lead to non-compliance with data protection regulations such as GDPR and CCPA. Non-compliance can result in hefty fines and legal implications. 

Without a formal review process, Shadow AI can easily breach compliance protocols, exposing the company to regulatory scrutiny and financial penalties.

Specific compliance issues include:

  • Data Privacy Violations: Shadow AI tools may not adhere to data privacy laws, leading to unlawful processing of personal data.
  • Inadequate Record Keeping: Organisations may struggle to maintain accurate records of data processing activities, a requirement under many data protection laws.
  • Cross-Border Data Transfers: Shadow AI might transfer data across borders without proper legal safeguards, violating international regulations.

Operational Risks

Shadow AI can disrupt business operations and decision-making. When departments use different AI tools without coordination, it can lead to discrepancies in data and analytics outcomes. This lack of consistency can affect strategic decisions and operational efficiency. 

According to the Dell Technologies survey, "44% of organisations are in early to mid-stages of GenAI deployment, often facing challenges in integrating these tools across departments"​​. This fragmented approach can lead to operational inefficiencies and misaligned strategies.

Operational risks include:

  • Data Silos: Different departments using separate AI tools can create data silos, preventing a unified view of business operations and customer insights.
  • Inconsistent Analytics: Without standardisation, analytics from Shadow AI tools can produce conflicting results, leading to poor decision-making.
  • Resource Wastage: Uncoordinated use of AI tools can result in redundant efforts and inefficient use of resources, impacting overall productivity.

Ethical Risks

AI models developed without oversight can embed biases, leading to unfair outcomes. This can harm the company's reputation and lead to ethical issues. Asana’s Work Smarter with AI playbook indicates that "81% of individual contributors fear that AI will compromise their human rights"​​. 

Ethical risks include bias in decision-making, lack of transparency in AI processes, and potential discrimination, all of which can damage a company's public image and trustworthiness.

Key ethical concerns include:

  • Bias and Discrimination: Unsupervised AI models can reinforce existing biases in data, leading to discriminatory practices in hiring, lending, or customer service.
  • Transparency Issues: Lack of oversight means AI processes and decisions are not transparent, making it hard to explain or justify outcomes to stakeholders.
  • Accountability Gaps: Without clear ownership and responsibility, it is challenging to address and rectify ethical issues arising from AI decisions.

How Zendata Helps Mitigate AI & Data Risks

Our CEO, Narayana Pappu, recently told DICE that “countering shadow A.I. is about understanding data flows. This includes knowing which employees or managers are accessing corporate data and how their teams use it within LLMs.”

In research released by LayerX, source code (31%), internal business information (43%) and Personal Identifiable Information (PII) (12%) are the leading types of pasted sensitive data - which makes Shadow AI a serious risk.

If you don’t understand how your business is using its data, where it’s stored, where it’s flowing to and from and what applications have access to it, you’ve lost the battle before it’s begun.

Let’s look at the 4 key ways Zendata can help to mitigate Shadow AI risk.

AI Governance

AI governance involves establishing a framework to oversee an organisation's development, deployment, and use of AI tools. Without proper AI governance, Shadow AI can flourish, leading to unauthorised AI usage, inconsistent practices and potential compliance violations. Effective AI governance ensures all AI activities are aligned with company policies and regulations, reducing the risk of Shadow AI.

Zendata automates the mapping and comparison of AI governance policies with CI/CD pipelines, AI model development, and deployment processes. This ensures that all AI activities adhere to the company's governance framework. By providing a unified, secure view of AI usage, Zendata helps businesses identify and address unauthorised AI activities, ensuring compliance and reducing the risk of Shadow AI.

Data Privacy Measures

Shadow AI can lead to significant security and privacy risks, including unauthorised access to sensitive data and data leakage and breaches. These risks arise because Shadow AI tools often bypass established security protocols, leaving data vulnerable to exposure and theft.

Zendata enhances data privacy and security by automating the identification, detection, management and protection of sensitive data across the entire IT infrastructure. This includes securing codebases, development pipelines, SDKs, endpoints and data lakes. By providing comprehensive coverage, Zendata helps businesses prevent data leakage and unauthorised data use, mitigating the security risks posed by Shadow AI.

Compliance Solutions

Shadow AI often leads to non-compliance with data protection regulations such as GDPR and CCPA, as these unauthorised tools and processes are not subjected to formal compliance checks. Non-compliance can result in hefty fines, legal consequences and damage to the organisation's reputation.

Zendata's compliance solutions include tools that highlight whether you’re processing data in the ways you say you are, along with tools that help businesses maintain accurate records of data processing activities. By integrating compliance checks into workflows, Zendata helps organisations avoid the legal and financial repercussions associated with Shadow AI.

AI Model Management

Unsupervised AI models developed and deployed without proper oversight can embed biases, leading to unfair and unethical outcomes. These biases can damage a company's reputation and result in ethical issues, including discrimination and lack of transparency in decision-making.

Zendata provides out-of-the-box remediation and integrated risk mitigation solutions for AI model management. These features allow businesses to monitor and manage their AI models to ensure they are developed and deployed in line with governance policies. By offering tools to detect and address bias, Zendata helps organisations maintain ethical standards and transparency, reducing the ethical risks associated with Shadow AI.

Why Current DLP Solutions Fall Short with Shadow AI

Traditional Data Loss Prevention (DLP) solutions often fall short when addressing the issues Shadow AI introduces. These tools typically focus on data at rest and in transit within known systems. 

However, Shadow AI tools frequently operate outside these monitored environments, using cloud services, third-party applications and unsanctioned tools, making it difficult for DLP solutions to detect and manage unauthorised data usage.

DLP solutions are not equipped to handle the diverse and dynamic nature of AI tools.

Most DLP solutions are reactive, focusing on identifying and responding to data breaches after they occur, rather than preventing unauthorised AI tools from being used in the first place. They also often lack integration with other security and governance tools, which is crucial for the effective management of Shadow AI.

Final Thoughts 

The rise of Shadow AI poses significant risks to businesses, including data security breaches, compliance violations, operational inefficiencies, and ethical dilemmas. Without proper oversight and governance, shadow AI can lead to severe financial, legal and reputational consequences. Addressing these risks is crucial for any organisation looking to leverage AI technology effectively.

By partnering with Zendata, organisations can turn the potential risks of Shadow AI into opportunities for secure, compliant and ethical growth. This proactive approach ensures that AI becomes a strategic asset that drives growth and not a liability that hinders it.

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Contact Us For More Information

If you’d like to understand more about Zendata’s solutions and how we can help you, please reach out to the team today.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.





Contact Us For More Information

If you’d like to understand more about Zendata’s solutions and how we can help you, please reach out to the team today.

Addressing Shadow AI Risks with Zendata AI Governance

June 12, 2024

Introduction

As businesses integrate artificial intelligence (AI) into their operations, Shadow AI has become a significant concern. Shadow AI refers to using AI tools and applications without formal oversight. This can lead to serious risks, including data breaches, compliance issues, operational inefficiencies and ethical dilemmas.

Managing these risks is essential for any organisation. Effective AI governance and strong security measures are vital to mitigating the potential downsides of Shadow AI. 

This article will discuss the business risks associated with Shadow AI and how Zendata provides comprehensive solutions to address these challenges, ensuring that businesses can safely and effectively harness the power of AI.

Understanding Shadow AI

Definition of Shadow AI

Shadow AI refers to using artificial intelligence applications, tools, and systems within an organisation without formal approval or oversight by the IT or data governance teams. These unauthorised AI implementations often occur because departments or individual employees seek to solve specific problems or gain insights quickly, bypassing standard protocols and controls.

Common Examples of Shadow AI in Businesses

Shadow AI can manifest in various ways within a business:

  • Third-party AI Tools: Employees might use third-party AI tools for data analysis, customer insights, or automating tasks without informing the IT department.
  • Custom AI Models: Teams might develop their own AI models using open-source frameworks, which are then deployed without proper security checks or integration into the company’s existing data infrastructure.
  • Cloud-based AI Services: Departments might subscribe to cloud-based AI services for specific projects, leading to potential data leakage or non-compliance with company policies.

Reasons for the Rise of Shadow AI in Organisations

Several factors contribute to the rise of Shadow AI:

  • Speed and Agility: Employees often turn to Shadow AI to quickly address pressing business needs without waiting for the slower, formal approval processes.
  • Accessibility of AI Tools: The increasing availability and ease of use of AI tools and platforms make it simple for non-technical staff to implement AI solutions independently.
  • Resource Constraints: Limited resources or budgets within IT departments can lead other departments to take initiative, using whatever tools they can access to meet their objectives.

Understanding Shadow AI is the first step towards recognising its risks and taking action to manage it effectively.

Business Risks Associated with Shadow AI

Several businesses, including companies like Samsung, have banned ChatGPT and other GenAI applications due to the risk of accidental (or intentional) data leakage. Back in 2023, Bloomberg reported that an internal Samsung survey found that 65% of respondents viewed GenAI as a security risk.

Data Security Risks

Shadow AI can lead to significant data security risks. When AI tools are used without proper oversight, there is a high chance of unauthorised access to sensitive data. According to a survey by LayerX, more than 6% of employees have pasted sensitive data into GenAI, directly putting their organisation at risk of data exfiltration.

This can result in data breaches, exposing the company to financial losses and reputational damage. The lack of centralised control makes it challenging to track and manage data flow, increasing the risk of data leaks.

Key issues include:

  • Unauthorised Data Access: Without IT oversight, employees may not follow best practices for data security, leading to potential breaches. Sensitive data could be accessed or transferred through insecure channels.
  • Lack of Encryption: Shadow AI tools may not have strong encryption, making data vulnerable during storage and transmission.
  • Inadequate Monitoring: Without centralised control, it is difficult to monitor and audit AI tool usage, increasing the risk of unnoticed breaches or data misuse.

Compliance Risks

Using AI tools without formal approval can lead to non-compliance with data protection regulations such as GDPR and CCPA. Non-compliance can result in hefty fines and legal implications. 

Without a formal review process, Shadow AI can easily breach compliance protocols, exposing the company to regulatory scrutiny and financial penalties.

Specific compliance issues include:

  • Data Privacy Violations: Shadow AI tools may not adhere to data privacy laws, leading to unlawful processing of personal data.
  • Inadequate Record Keeping: Organisations may struggle to maintain accurate records of data processing activities, a requirement under many data protection laws.
  • Cross-Border Data Transfers: Shadow AI might transfer data across borders without proper legal safeguards, violating international regulations.

Operational Risks

Shadow AI can disrupt business operations and decision-making. When departments use different AI tools without coordination, it can lead to discrepancies in data and analytics outcomes. This lack of consistency can affect strategic decisions and operational efficiency. 

According to the Dell Technologies survey, "44% of organisations are in early to mid-stages of GenAI deployment, often facing challenges in integrating these tools across departments"​​. This fragmented approach can lead to operational inefficiencies and misaligned strategies.

Operational risks include:

  • Data Silos: Different departments using separate AI tools can create data silos, preventing a unified view of business operations and customer insights.
  • Inconsistent Analytics: Without standardisation, analytics from Shadow AI tools can produce conflicting results, leading to poor decision-making.
  • Resource Wastage: Uncoordinated use of AI tools can result in redundant efforts and inefficient use of resources, impacting overall productivity.

Ethical Risks

AI models developed without oversight can embed biases, leading to unfair outcomes. This can harm the company's reputation and lead to ethical issues. Asana’s Work Smarter with AI playbook indicates that "81% of individual contributors fear that AI will compromise their human rights"​​. 

Ethical risks include bias in decision-making, lack of transparency in AI processes, and potential discrimination, all of which can damage a company's public image and trustworthiness.

Key ethical concerns include:

  • Bias and Discrimination: Unsupervised AI models can reinforce existing biases in data, leading to discriminatory practices in hiring, lending, or customer service.
  • Transparency Issues: Lack of oversight means AI processes and decisions are not transparent, making it hard to explain or justify outcomes to stakeholders.
  • Accountability Gaps: Without clear ownership and responsibility, it is challenging to address and rectify ethical issues arising from AI decisions.

How Zendata Helps Mitigate AI & Data Risks

Our CEO, Narayana Pappu, recently told DICE that “countering shadow A.I. is about understanding data flows. This includes knowing which employees or managers are accessing corporate data and how their teams use it within LLMs.”

In research released by LayerX, source code (31%), internal business information (43%) and Personal Identifiable Information (PII) (12%) are the leading types of pasted sensitive data - which makes Shadow AI a serious risk.

If you don’t understand how your business is using its data, where it’s stored, where it’s flowing to and from and what applications have access to it, you’ve lost the battle before it’s begun.

Let’s look at the 4 key ways Zendata can help to mitigate Shadow AI risk.

AI Governance

AI governance involves establishing a framework to oversee an organisation's development, deployment, and use of AI tools. Without proper AI governance, Shadow AI can flourish, leading to unauthorised AI usage, inconsistent practices and potential compliance violations. Effective AI governance ensures all AI activities are aligned with company policies and regulations, reducing the risk of Shadow AI.

Zendata automates the mapping and comparison of AI governance policies with CI/CD pipelines, AI model development, and deployment processes. This ensures that all AI activities adhere to the company's governance framework. By providing a unified, secure view of AI usage, Zendata helps businesses identify and address unauthorised AI activities, ensuring compliance and reducing the risk of Shadow AI.

Data Privacy Measures

Shadow AI can lead to significant security and privacy risks, including unauthorised access to sensitive data and data leakage and breaches. These risks arise because Shadow AI tools often bypass established security protocols, leaving data vulnerable to exposure and theft.

Zendata enhances data privacy and security by automating the identification, detection, management and protection of sensitive data across the entire IT infrastructure. This includes securing codebases, development pipelines, SDKs, endpoints and data lakes. By providing comprehensive coverage, Zendata helps businesses prevent data leakage and unauthorised data use, mitigating the security risks posed by Shadow AI.

Compliance Solutions

Shadow AI often leads to non-compliance with data protection regulations such as GDPR and CCPA, as these unauthorised tools and processes are not subjected to formal compliance checks. Non-compliance can result in hefty fines, legal consequences and damage to the organisation's reputation.

Zendata's compliance solutions include tools that highlight whether you’re processing data in the ways you say you are, along with tools that help businesses maintain accurate records of data processing activities. By integrating compliance checks into workflows, Zendata helps organisations avoid the legal and financial repercussions associated with Shadow AI.

AI Model Management

Unsupervised AI models developed and deployed without proper oversight can embed biases, leading to unfair and unethical outcomes. These biases can damage a company's reputation and result in ethical issues, including discrimination and lack of transparency in decision-making.

Zendata provides out-of-the-box remediation and integrated risk mitigation solutions for AI model management. These features allow businesses to monitor and manage their AI models to ensure they are developed and deployed in line with governance policies. By offering tools to detect and address bias, Zendata helps organisations maintain ethical standards and transparency, reducing the ethical risks associated with Shadow AI.

Why Current DLP Solutions Fall Short with Shadow AI

Traditional Data Loss Prevention (DLP) solutions often fall short when addressing the issues Shadow AI introduces. These tools typically focus on data at rest and in transit within known systems. 

However, Shadow AI tools frequently operate outside these monitored environments, using cloud services, third-party applications and unsanctioned tools, making it difficult for DLP solutions to detect and manage unauthorised data usage.

DLP solutions are not equipped to handle the diverse and dynamic nature of AI tools.

Most DLP solutions are reactive, focusing on identifying and responding to data breaches after they occur, rather than preventing unauthorised AI tools from being used in the first place. They also often lack integration with other security and governance tools, which is crucial for the effective management of Shadow AI.

Final Thoughts 

The rise of Shadow AI poses significant risks to businesses, including data security breaches, compliance violations, operational inefficiencies, and ethical dilemmas. Without proper oversight and governance, shadow AI can lead to severe financial, legal and reputational consequences. Addressing these risks is crucial for any organisation looking to leverage AI technology effectively.

By partnering with Zendata, organisations can turn the potential risks of Shadow AI into opportunities for secure, compliant and ethical growth. This proactive approach ensures that AI becomes a strategic asset that drives growth and not a liability that hinders it.