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Generative AI Risks & Their Impact On Enterprise Wide GenAI Adoption

genai risks

Enterprise technology is being overhauled by Generative AI, with new use cases emerging to streamline core business operations across various industries. As a transformative force, GenAI promises to drive productivity, efficiency, and innovation, and many firms have interpreted this as a green flag for their investment in these cutting-edge solutions. 

With widespread enterprise-level Generative AI adoption comes a degree of caution, and 64% of respondents in the Salesforce State of IT Survey expressed exactly this, voicing a specific wariness around the ethics of the technology. Portal26 is setting out to assess potential Generative AI risks, as well as understanding some of the gray areas that exist in user adoption. 

The uncharted risks of Generative AI

In the same survey, an astounding 86% of IT leaders cited that they believe Generative AI will play a prominent role in the near future within their organizations, indicating clear undertones of positivity in the way key figures view the technology. However, doubt is still manifesting in various formats, and this creates a gray area between advocacy and wariness, where GenAI is creating some uneasy food for thought. Some of the Generative AI risks that exist within this space include:

Ethical dilemmas & bias

Understanding the ethical considerations surrounding GenAI is essential, and operating without this kind of AI governance can have a detrimental impact on enterprises. Whether they’re created intentionally or unintentionally, perpetual biases can become embedded in AI algorithms, leading to skewed, discriminatory decision-making processes.

Intellectual property & ownership

GenAI introduces challenges to intellectual property rights, as it generates content autonomously. Claiming ownership of AI-generated content requires accounting for a selection of complexities, and these insights will be fundamental if a firm ends up in a legal entanglement in regards to IP.

Cybersecurity & misuse

As with any new advancement, the technology gives way to a variety of potential threats, with cybersecurity activity being one of the most serious outcomes. Organizations must address this with a robust, thorough, multi-faceted cybersecurity protocol for all sensitive data, and this needs to apply to both internal and external data.

Fake content propagation

One of the trickiest outcomes to navigate in relation to GenAI ethics is the surfacing of deepfakes and consequent misinformation. When GenAI inputs aren’t properly governed, as a result of general lack of education or set standards, deepfakes can be produced. Organizations then become accountable for recognizing and combating the spread of false information, and this warrants having innovative solutions to detect and counteract the proliferation of fake content.

Shadow AI

Shadow AI, unsanctioned and ad-hoc use of GenAI outside of IT governance, poses a significant risk to data security and integrity – and it’s another outcome that can often happen unintentionally. Often, a lack of education across those who are deploying the AI technology is the culprit for shadow AI, but in some cases it can occur out of malicious practice. Organizations need to be proactive in both instances, ensuring total visibility and adherence to acceptable application standards. 

This is exactly where GenAI ethics and governance methods, such as definitive usage policies, serve as a solution – but in order to tackle this kind of incorrect usage, these actions need to be embodied fully and accessible to everyone in the enterprise.

Malicious use of generated data

As alluded to above, GenAI’s ability to generate vast amounts of data introduces potential risks of malicious use. When this is the case, sensitive information and critical assets can be jeopardized, and the impact for enterprises can be difficult to recover from in terms of the trust lost between both consumers and key stakeholders. A capable GenAI privacy and security platform will ensure constant encryption, secure data residency, and access restrictions to avoid this kind of unwanted eventuality.

Human-AI collaboration and mitigation

So, let’s factor humans into this equation – but let’s delve a bit deeper than the recent ‘could robots really take our jobs?’ debacle. While 62% of the Salesforce survey respondents admitted that they’re worried about the impact that GenAI could have on their career, in most cases, applications are intended to run alongside the human workforce, helping them to be more efficient and dynamic. We’ve listed some key themes for enterprises to consider when it comes to navigating human-AI collaboration successfully.

AI ethical guidelines and governance

Establishing ethical guidelines (referred to as Generative AI governance) has become a non-negotiable for organizations as they embrace human-AI collaboration at a growing rate. These kinds of ‘best practice’ guidelines set the standard for all usage, helping to avoid perpetuating biases, and encouraging fairness in outputs. Ethical considerations can also mitigate any potential liabilities or legal consequences of GenAI usage, as they’re set out to make sure application adheres to necessary laws and regulations. 

Human-AI collaboration can also present complex ethical dilemmas, such as uncertainty around accountability for the resultant output, or risking any IP infringements. Establishing guidelines provides a structured approach for addressing these dilemmas, guiding decision-makers in navigating situations where ethical considerations are at play. The ultimate goal of these application guidelines is to encourage responsible usage of Generative AI, and organizations have the power to set these boundaries.

Legal reforms and intellectual property

As GenAI continues to advance, traditional legal frameworks are being challenged to keep up with the pace set by these new solutions. These reforms should encompass every facet of the technology – from ethical considerations, to targeting AI and privacy issues.

Applying Intellectual Property laws to AI

Intellectual property laws play a pivotal role in governing the rights and ownership of GenAI content, covering issues related to authorship, ownership, and the protection of AI-generated outputs.

Examples of legal frameworks addressing AI-related issues

Some notable examples where AI is invoked by legal clauses include the GDPR laws that apply in the European Union, the Algorithmic Accountability Act in the United States, and the Personal Information Protection Law in China.

In all of these examples, the safety and usage of AI generated data was scrutinized, with each law enforcing a form of protection against these outputs. 

Detecting misleading or harmful content

Some of the main methodologies used to tackle the dissemination of IP, misleading, or harmful content include:

    • Advanced algorithms and machine learning – Sophisticated algorithms and machine learning models allow organizations to analyze content patterns to identify potential instances of misinformation, bias, or harmful intent.
    • Natural Language Processing (NLP) techniques – NLP techniques are used to understand the context and sentiment of written content. By analyzing language nuances, NLP can identify misleading information or harmful intent.
    • Image and video analysis – AI-powered image and video analysis tools can identify manipulated or deepfake content, helping to detect instances where visual information is used to deceive or harm users.
    • Human oversight and verification – Human oversight is still a vital way to root out malicious content, as AI systems might not always capture the subtleties of certain types of misinformation.

Gen AI risks: The global ethical and regulatory landscape

Moving our focus onto ethical and regulatory considerations, we’re examining some of the GenAI risks that are shaping the way enterprises adopt these in-demand technologies around the world, without compromise.

Challenges & opportunities in harmonizing AI regulations globally

Understanding AI’s place within diverse legal traditions, and culturally-charged approaches to regulation present challenges across different parts of the world. Legal systems, cultural norms, and historical contexts vary, making it difficult to devise a one-size-fits-all regulatory framework. What might be considered ethical in one cultural context may differ in another, so refined approaches to ethical AI development and deployment become necessary.

Varying technological capabilities also impact the pace of AI adoption in this sense, and streamlining regulations requires accommodating these differences while ensuring that standards are robust enough to address potential Generative AI risks.

There are plenty of opportunities to seize though, from making use of shared knowledge between nations and cultures, using these insights to fuel AI innovation in a way that respects different global traditions.

Public awareness, trust and transparency in Generative AI risk education

As mainstream GenAI integration rises, it’s becoming essential for the public to understand the dangers of artificial intelligence. Awareness empowers individuals to make informed decisions about their own applications, but it also comes with its own set of ethical factors to account for. Public awareness initiatives are being used to highlight the ethics of GenAI, including issues related to bias, privacy, and the responsible use of AI-generated content.

Initiatives for educating the general public such as outreach programs can also give consumers a first-hand experience of utilizing the technology,  and these workshops are often tailored to reach diverse audiences – adding a degree of accessibility to AI applications. Online webinars are another format for breeding public awareness of Gen AI development, and they can cover a breadth of topics around GenAI risks and uses. 

GenAI adoption entails a range of risks, from ethical considerations to cybersecurity threats. In this respect, strategic mitigation is the answer for companies that want to reap the benefits of these ultra-capable solutions.

We’re optimistic about the future of GenAI, and embracing responsible, ethical practices is integral for successful implementation. Our GenAI visibility platform is designed to empower organizations to effectively manage and mitigate Generative AI risks, while also providing other invaluable operational insights. 

The journey to enterprise-wide GenAI adoption is a collective effort. Let’s shape a future where innovation and responsibility coexist seamlessly. Find out more about Generative AI use policies by scheduling a demo with our experts today. 

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