Introduction

The rapid adoption of AI tools like Microsoft Copilot in business operations has brought significant benefits and notable governance challenges. Businesses increasingly integrate these tools to streamline operations, enhance decision-making and stay competitive. However, this integration brings complex issues that must be managed effectively to avoid potential risks.

Narayana Pappu, CEO of Zendata, believes that over the last few years, “...changes to the data ecosystem from the increasing numbers of third-party integrations, and now from AI, have increased acceptance of risks within organisations."

IBM’s 2023 Leadership In The Age of AI report found that “nearly 80% of UK businesses have already deployed generative AI in their business or are planning to within the next year”. This rush to implement internal AI models and GenAI tools exposes businesses to traditional data and cybersecurity risks and new AI/LLM-specific ones.

This article examines the data risks and security challenges stemming from the rapid adoption of AI tools like Copilot in business operations. It highlights the dangers of enabling such technologies without proper governance, including data breaches, unauthorised access and unpredictable AI behaviour.

The Current AI Landscape

Businesses are rapidly integrating AI tools into corporate environments, driven by the promise of increased efficiency and competitive advantage. However, this rapid deployment needs more oversight than it currently gets, leading to significant security vulnerabilities. The push for speed in adopting new technologies can bypass essential steps such as thorough testing and comprehensive risk assessments, leaving systems open to potential breaches. 

In some instances, organisations like the US Congress have banned GenAI tools like Copilot and ChatGPT due to the risk of leaking sensitive Government data to “non-House-approved cloud services.” However, this opens up a different set of risks because staff will undoubtedly look for workarounds to continue accessing these tools, like sending data to their personal email addresses to continue working at home or downloading things to hard drives, which could easily be lost.

It’s for this reason that continuous monitoring and relevant controls like Data and AI Security Posture Management tools are essential. Without them, it would be virtually impossible to keep track of everything going on within your IT infrastructure (more on this later).

Access and Oversight

An important aspect of AI integration is determining which users can access these powerful tools, especially regarding their corporate or sensitive data access. Without proper oversight, there is a risk that AI tools could be misused or mishandled, potentially leading to data exposure, leakage and other forms of cyber threats. Businesses must ensure that only authorised personnel with a legitimate need can access and use AI functionalities, aligning access with user roles and data security protocols.

Lack of Governance and Guardrails

The need for robust governance structures and clear 'guardrails' is a glaring issue in the current AI landscape. Businesses often rush to deploy AI solutions without establishing the necessary frameworks to ensure these tools operate within safe and ethical parameters. This oversight can lead to AI systems making unpredictable decisions or taking actions that could harm the business or its customers. Implementing strict governance policies and creating continuous monitoring and control mechanisms are crucial to managing AI tools effectively and securely.

CrowdStrike Auto-Update Issue

Although unrelated to AI, the CrowdStrike incident is a pertinent example of how unchecked and untested auto-updates can lead to significant issues. In this case, an unattended auto-update was applied without thorough vetting, leading to widespread system disruptions of around 8.5 million machines, according to the BBC. This example underscores the critical need for stringent testing and governance of all automated processes, including AI deployments. It highlights the potential knock-on effects when new technologies are implemented without adequate oversight and risk management.

Copilot and Emerging AI Security Concerns

Integrating Microsoft’s Copilot in business operations has surfaced several security concerns, emphasising the need to manage AI tools carefully. There are several issues to consider, but some of the key ones include:

• Data Access and Control: Concerns arise about which Copilot-enabled users and AI accounts can access sensitive data, raising the risk of unauthorised data breaches if access controls are not adequately managed.
• System Vulnerabilities: Introducing AI tools like Copilot may lead to vulnerabilities due to misconfigurations or inadequate security measures, exposing businesses to potential data exposure and system breaches.
• Shadow IT: The deployment of AI tools can result in the creation of shadow users or accounts with permissions that bypass traditional IT security controls, increasing the risk of insider threats and data leakage.

These concerns underscore the potential risks associated with the unchecked use of AI tools in business environments, leading to significant security and compliance issues.

Earlier this year, in a LinkedIn post, Gartner analyst Avivah Litan highlighted two major business risks associated with Copilot, which you can see in the image below.

1. Hallucinations
2. LLM access to the entire corporate database.

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As Litan says in the post, locking down files is nothing new for IT teams - the real risk stems from what prompts are fed into the model and what they could draw out of the data unwittingly.

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In his LinkedIn article, The Hidden Risks of Deploying AI Assistants: Protecting Sensitive Data with Microsoft Copilot, Justin Endres, CRO of Seclore, lists some simple prompts that could expose confidential, sensitive data and cause serious reputational, financial and legal damage to businesses. 

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More importantly, he highlights that “…you ALSO must consider how you protect your sensitive data when it sits in the hands of your 3rd party suppliers, vendors, etc. Ensuring your blast radius doesn’t grow means when sensitive data is shared intentionally (or unintentionally),, the data must be protected persistently”

Implementing Solutions to Enhance AI Security Posture Management

In response to these challenges, businesses are turning to comprehensive solutions that extend beyond traditional guardrails for AI tools:

• AI Access Intelligence: This solution enhances control over who can access sensitive data by providing a bi-directional view of data access permissions. It allows businesses to automatically revoke stale or excessive permissions, reducing the risk of unauthorised access without disrupting operations.
• AI Security Posture Management (AISPM): AISPM helps detect and manage potential security risks like misconfigurations, shadow users, and open permissions. It is critical in remediating vulnerabilities such as public S3 buckets, weak password policies, and outdated data, safeguarding against data exposure and abuse.

These solutions do not directly alter or control AI systems like CoPilot or LLMs but rather enhance the security framework within which these AI tools operate. By strengthening the overall security posture, businesses can ensure that their AI deployments are effective and secure, aligning with best practices and compliance standards.

Defining AI Guardrails and Governance

Concept of AI Guardrails

Ade Taylor, Head of Security Services at ROC Technologies, believes that businesses are rushing to deploy GenAI tools due to pressures from senior management to avoid getting left behind. He says that “...there are two main questions that I get asked: ‘Can I safely turn Copilot on?’ and ‘I’ve turned Copilot on - am I exposed to risks?’ Businesses are more aware of things like hallucinations and, for those that are building their own LLMs internally, the’re now wondering whether it’s already compromised or whether it’s hallucinatory.”

This is where AI Guardrails come in. 

AI guardrails are mechanisms and policies designed to ensure AI tools operate within predefined safe parameters. They are crucial for preventing AI systems from making unpredictable or harmful decisions. These guardrails are essential in maintaining the integrity and reliability of AI operations within business environments, ensuring that they contribute positively to business processes without causing unintended disruptions or security breaches.

Importance of Transparency and Control

Effective AI guardrails should be underpinned by transparency and control, which involve:

• Detailed Logging and Monitoring: Systems should be implemented to track AI behaviour continuously. This includes logging all decisions made by AI systems and monitoring their operations in real time to quickly identify and address deviations from established norms.
• Policy Enforcement: Businesses should develop and enforce strict policies that define acceptable AI behaviours and responses. These policies serve as a framework within which AI systems must operate, aligning their functionality with ethical standards and business objectives.

Examples of AI Guardrails

Implementing AI guardrails involves several practical measures designed to safeguard AI operations:

Operational Boundaries

Clearly define the scope and limits of AI operations. Based on thorough risk assessments, AI systems should have specific parameters within which to function.

Decision Validation Protocols

Implement secondary checks or supervisory approvals for critical AI decisions. This ensures that humans or additional automated systems review and validate AI actions before execution.

Transparency and Explainability

Ensure AI systems are transparent and can explain their decision-making processes. This helps in understanding how decisions are made and in identifying potential biases or errors in the logic.

Continuous Monitoring and Auditing

Set up real-time monitoring systems to track AI behaviour and decisions. Conduct regular audits to ensure compliance with established policies and identify deviations or anomalies.

Ethical Guidelines

Develop and enforce ethical guidelines that the AI must follow. These guidelines should cover fairness, accountability, and avoiding biased or discriminatory outcomes.

Access Controls

Implement strict access controls to ensure only authorised users can interact with the AI systems. This includes managing permissions and regularly reviewing access levels to prevent unauthorised use.

Feedback and Learning Mechanisms

Establish feedback loops where AI systems can learn from past decisions and adjust their behaviour accordingly. However, this learning should be supervised to prevent the AI from reinforcing harmful behaviours.

Security Measures

Incorporate robust security protocols to protect AI systems from external threats. This includes protecting data integrity, ensuring secure data transmission, and safeguarding against data poisoning or model tampering.

Compliance and Legal Standards

Ensure that AI operations comply with relevant legal and regulatory standards. This includes adhering to data privacy laws and industry-specific regulations.

Challenges in Implementing AI Guardrails

Implementing AI guardrails presents several challenges businesses must navigate to ensure these mechanisms effectively safeguard AI operations. These challenges stem from the dynamic nature of AI technologies and the complexities involved in their management and oversight. Here are some critical challenges discussed:

Rapid Deployment Pressure

Businesses are under significant pressure to deploy AI technologies swiftly to stay competitive. This rush can often result in implementing AI solutions without fully establishing the necessary guardrails. The focus tends to be on AI's immediate benefits, with less attention given to its long-term integration implications, especially concerning security and compliance.

Complexity of AI Systems

AI systems, particularly LLMs and advanced decision-making tools, are inherently complex. Their complexity can make predicting all possible outcomes or behaviours difficult, complicating setting effective operational boundaries. This complexity also challenges the establishment of clear and effective policies that can cover all potential scenarios.

Lack of Transparency

AI systems can sometimes operate as "black boxes," where the decision-making process is not transparent. This lack of transparency makes it hard to implement guardrails that require an understanding of how decisions are made. Without this understanding, enforcing policies or verifying that the AI operates within safe and ethical parameters is challenging.

Integration with Existing Systems

Integrating AI guardrails into IT and business systems poses technical and operational challenges. Existing systems may not be designed to accommodate the controls and checks required by robust AI guardrails, requiring significant adjustments or redesigns.

Ensuring Continuous Monitoring and Adaptation

AI technologies evolve rapidly, as do the potential associated threats. Guardrails must be adaptable and regularly updated to respond to new risks. Continuous monitoring and dynamically adjusting guardrails are essential but can be resource-intensive and technically challenging.

Implementing AI in Large Enterprises

Implementing artificial intelligence (AI) tools like CoPilot in large enterprise environments requires addressing unique challenges, careful management, strategic planning, and robust AI governance frameworks. The complexity of large-scale IT infrastructures, the diversity of systems, and extensive operational needs play a significant role in managing AI integration.

Enterprise Implementation Challenges

Large enterprises encounter several specific challenges when integrating AI applications:

• System Compatibility: Ensuring that AI tools are compatible with various legacy systems and software platforms requires extensive customisation. This can lead to significant deployment delays and may necessitate iterative testing and adaptation to ensure full integration without disrupting existing workflows.
• Data Governance: Large enterprises handle vast amounts of data, requiring effective data governance to ensure compliance with international privacy laws and regulations. Establishing data usage, access and storage protocols is crucial to prevent misuse and maintain data integrity across AI interactions. This involves not only technical solutions but also policy-driven approaches to data management.
• Change Management: Integrating AI tools necessitates significant changes to existing workflows and processes. Effective change management requires careful planning and stakeholder engagement to ensure all employees understand AI integration's benefits and potential impacts. This includes training programs, regular communication, and feedback mechanisms to adjust the integration process.

Continuous Monitoring and Dynamic Control

For effective management of AI tools in large enterprises, continuous monitoring and dynamic control are essential:

• Real-Time Assessments: Enterprises must continually assess AI system performance to ensure they function as intended and do not introduce vulnerabilities. This involves setting up monitoring systems to track AI decisions and actions in real time, allowing for immediate intervention if deviations from established norms are detected.
• Dynamic Adjustments: AI systems can evolve based on new data and interactions, necessitating dynamic adjustments to their operational parameters. This requires systems that can detect changes to behaviour and adapt controls automatically to maintain security, performance, and compliance.
• Feedback Loops: It is critical to implement feedback mechanisms that allow AI systems to learn from their operations and adjust their processes accordingly. This should be managed carefully to avoid unintended consequences of self-learning systems.

Strategic Implementation for Enhanced Security and Governance

Enhancing security and governance when implementing AI involves a multi-faceted approach:

• Robust Security Protocols: Implementing strong security measures, including encrypted data transmission and secure data storage, is essential. Regular security audits and vulnerability assessments help safeguard sensitive information and protect against emerging cyber threats.
• Data Privacy and Leakage Prevention: Preventing data leakage is crucial. This includes setting up systems to monitor how AI tools access and use data to prevent unauthorised access and minimise data exposure.
• AI Transparency and Ethical Governance: AI transparency is vital to maintaining trust and accountability. Enterprises should implement ethical AI frameworks that outline clear guidelines on AI behaviour, focusing on fairness, responsibility, and transparency.
• LLM Security: Security for Large Language Models involves specific strategies to mitigate risks like data poisoning and model theft. These include ensuring controlled data access, conducting regular integrity checks, and employing techniques like differential privacy to protect data used by these models.

The Future of AI Governance

As AI technology continues to evolve and integrate more deeply into business operations, the future of AI governance is poised to become more sophisticated and integral to maintaining organisational integrity and competitiveness. 

In her article for Computer Weekly, Svetlana Sicular, VP Analyst at Gartner, writes that “Although seemingly a paradox, good governance actually enables better innovation. It provides the constraints and guardrails that give organisations the ability to explore questions about AI’s value and risks, as well as the space within which to innovate and start to produce results.”

Developing new governance standards and practices is essential to manage the increasing complexity and potential risks associated with AI tools like Copilot.

The Role of AI TRiSM in AI Governance

AI TRiSM (AI Transparency, Risk, and Security Management) is essential to effective AI governance because it provides a structured framework to address the complexities and risks associated with AI technologies. By focusing on transparency, AI TRiSM ensures that AI systems operate in an understandable and accountable manner. Risk management components help identify and mitigate potential threats, while security measures protect against data breaches and unauthorized access. Together, these elements ensure that AI tools are implemented safely and ethically, aligning with business goals and regulatory requirements.

Challenges and Necessity for New Governance Standards

As the use of AI grows, so does the complexity of ensuring its secure and ethical application. This development necessitates new governance standards that specifically address the challenges posed by AI technologies:

• Regulatory Compliance: As regulations evolve, maintaining compliance requires adaptive governance frameworks that quickly adjust to new legal and ethical standards.
• Ethical AI Usage: Ensuring AI is used ethically involves ongoing assessments of how AI decisions are made and the implications of these decisions, particularly in terms of privacy, fairness, and transparency.

Advocating for Robust AI Governance Frameworks

The integration of AI into security systems must be accompanied by robust governance frameworks that ensure AI tools do not undermine organisational goals or ethical standards.

• Continuous Oversight and Review: Regular audits and reviews of AI systems help ensure they function as intended and adhere to ethical guidelines and business objectives.
• Stakeholder Engagement: Engaging stakeholders in the development and implementation of AI governance frameworks ensures that the interests and concerns of all parties are considered, promoting broader acceptance and compliance.

Final Thoughts

Adopting AI tools like Microsoft Copilot offers tremendous opportunities for businesses to enhance efficiency and decision-making. However, this rapid integration also introduces significant risks that must be managed carefully. AI governance and ethical standards are essential to prevent data breaches, unauthorised access and unpredictable AI behaviour.

Comprehensive AI guardrails will help businesses maintain control over these powerful technologies, ensuring they contribute positively without causing unintended harm. As we navigate this evolving landscape, companies must adopt a cautious and well-regulated approach to AI, balancing innovation with security and ethical considerations. By doing so, organisations can fully leverage AI’s potential while protecting their most valuable assets: their data and reputation.

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