Impact of AI on Cybersecurity

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The Paradox of AI: Navigating the Intersection of Generative AI and Cybersecurity Concerns

Most executives that I speak to admit deep concern about the security risks of generative AI. This includes exposing sensitive company information and result in the total loss of control of core business functions. With each technological breakthrough and its applications, there exists a fundamental risk: our ambitions may outpace our control. The advent of the internet marked a seismic shift in communication and commerce, yet it also ushered in novel threats like identity theft, data breaches, and cybercrime. It took a considerable amount of time before laws such as the Children’s Online Privacy Protection Act (COPPA) and the Digital Millennium Copyright Act (DMCA) emerged to hold companies accountable for threat mitigation.

This trend of technological advancements outstripping regulatory measures is evident once again with artificial intelligence (AI). Consider ChatGPT as an example. This language model, based on the generative pre-trained transformer (GPT) architecture, is adept at understanding and generating text that closely resembles human communication. Trained on extensive internet text data, ChatGPT can respond to queries, create content, and assist in various tasks by predicting the most suitable subsequent word in a sequence. This enables it to generate novel and unique outputs. Known as “generative AI”, it is a branch of AI capable of creating new content, including images, code, text, art, and music, by learning from patterns in existing data.

Generative AI stands out for its rapid pace of innovation and its swift democratization and accessibility. A study by The Conference Board found that 56% of workers are currently utilizing or planning to use generative AI in their daily tasks. Despite its numerous advantages, the widespread adoption of generative AI introduces challenges in ethical usage, data privacy, and security. As AI models advance — which they inevitably will — the potential for misuse or unforeseen consequences increases, underscoring the need for stringent oversight and proactive governance. The race between innovation and regulation is underway, with higher stakes than ever before.

The Overlooked Risks of Generative AI

Discussions often focus on potential biases and adverse outcomes from flawed AI data inputs, but a critical, often overlooked threat is the heightened cybersecurity risks. AI technologies inherently could escalate the sophistication of cyberattacks. Simple chatbots, for example, might unintentionally facilitate phishing schemes, create error-free fake social media profiles, or adapt malware to target various programming languages. Additionally, the extensive data these systems process can be stored and potentially shared with third parties, elevating the risk of data breaches.

With tools like ChatGPT accessible to anyone with an internet connection and regulatory frameworks lagging, businesses may unknowingly expose themselves to a plethora of unforeseen threats.

Responsibility for Generative AI

The unfolding potential of generative AI has already sparked significant concerns regarding security, data management, and compliance. The rapid development of AI has outpaced the evolution of regulatory frameworks and policy controls, creating a gap in accountability and transparency. This places the responsibility squarely on businesses to act as the primary guardians of security controls and frameworks.

The appeal of AI, particularly generative AI, is undeniable. However, with such easy access, employees might unintentionally enter sensitive or proprietary information into free AI tools, leading to numerous vulnerabilities. These vulnerabilities could result in unauthorized access or accidental exposure of confidential business information, including intellectual property and personal data.

According to a report by Security Magazine, 93% of companies recognize the risks associated with using generative AI inside the enterprise. However, only 9% say they’re prepared to manage the threat. The urgency for establishing clear AI usage guidelines will only rise as the technology continues to accelerate in capability and scope.

With such easy access to Generative AI tools, employees might unintentionally enter sensitive or proprietary information into free AI tools, leading to a multitude of vulnerabilities.

Top Generative AI Concerns for Organizations

  1. Employees making decisions based on inaccurate information.
  2. Employee misuse and ethical risks
  3. Leaking sensitive information: Generative AI models learn from large datasets, which might contain sensitive data, depending on what information is shared. If not properly handled, there’s a risk of employees inadvertently revealing confidential information through generated outputs. AI models can store this information too, making your sensitive data accessible to anyone who accesses your user account with different AI tools.
  4. Intellectual property: Generative models often pull in a massive amount of publicly available information, including exposed proprietary data. There is also a risk that your intellectual property could end up being used in AI tools if it’s not secure.

Actions for Risk Managers to Bolster Security

Businesses may not be able to alter the rules of the game, but they can limit their exposure and decide their level of participation. Here are several strategies that risk managers can and should employ to address the vulnerabilities associated with generative AI.

1. Identify AI Utilization

Risk managers should start by identifying which employees are using AI tools and for what purposes. This can be done through internal audits, surveys, and monitoring endpoints to track tool usage. The goal is not to catch employees off guard but to understand the demand for AI tools and their potential value.

2. Perform a Business Impact Analysis

The next step involves a comprehensive analysis to evaluate the value of each AI application. This includes assessing its benefits, potential security risks, and privacy concerns. Understanding why employees are adopting specific AI tools and the benefits they bring to the business is crucial. Sometimes, adjusting the tool’s data access permissions may make its benefits outweigh the risks, allowing its integration into the organization’s technology stack.

3. Implement Governance

Following the analysis, AI tool usage should align with your company’s policies and risk posture, rather than being left to individual discretion. This may involve creating controlled environments for testing AI technologies and their associated risks. Employees should be encouraged to explore new AI applications, but these should be tested within the company framework before wider deployment. Monitoring and reviewing AI outputs, especially in the initial stages, is also crucial.

In addition, companies need to implement an acceptable use policy.

FREE Download: Generative AI Employee Acceptable Use Policy Template.

Generative AI employee usage policy

4. Conduct Training and Awareness

Ensuring that every organization member, regardless of their technical background, understands the risks associated with AI technologies is vital. Regular training sessions and workshops will keep the workforce informed about the threats and challenges posed by AI tools to the organization as a whole.

5. Classify Data

Working with chief information security officers (CISOs), technology teams, and enterprise risk management to classify data is essential. This helps determine which datasets are safe for AI tool use and which pose significant risks. For example, highly sensitive data can be restricted from certain AI tools, while less sensitive data can be used more freely. Data classification is a fundamental aspect of sound data hygiene and security.

6. Prepare for Regulatory Changes

Considering the unregulated nature of AI, businesses should prepare for inevitable regulatory oversight. Keeping abreast of global AI regulations and standards will enable businesses to adapt quickly. Over-reliance on a specific tool or making it central to business operations is risky at this stage. For now, AI tools should be viewed as supportive business tools rather than operational drivers.

The integration of AI technologies into business operations is inevitable. While these technologies offer unparalleled benefits, they also introduce a host of cybersecurity challenges. However, by proactively identifying and mitigating these risks and adopting new technology in a controlled and well-governed manner, businesses can leverage the power of AI without jeopardizing their security stance.


It’s undeniable that AI has revolutionized the global landscape, and its evolution is set to continue in the forthcoming years.

Grasping the advantages and challenges associated with AI, equipping staff with the skills to effectively utilize various AI technologies, and establishing clear guidelines on what is permissible to share, marks the beginning of this journey.

The major concern for me revolves around the privacy and compliance issues raised by AI. It’s a technology that’s here to stay. We’re going to witness its increasing presence, so it’s crucial to have robust policies and procedures in place for AI usage, along with providing employees with guidance on the potential consequences of employing AI tools.