Private AI vs Public AI: The Risks and Rewards of Connecting Your Data

Navigating the Equation: Data, Effectiveness, and Risk

It’s imperative to understand the Data-Efficiency Risk Formula—a mathematical representation of the interplay between data quantity (Q), AI effectiveness (E), and risk exposure (R). This equation lays bare the trade-offs and considerations that lie at the heart of AI integration.

The Data-Efficiency Risk Formula: E = f(Q) / R

• Effectiveness (E): The proficiency of AI in its tasks, often bolstered by increased data.

• Quantity of Data (Q): The volume and diversity of data accessible to AI.
• Risk Exposure (R): The encompassing vulnerabilities and security concerns.

 

This formula thus proves that there’s an increased effectivity and risk that grows linearly and in parallel as more quality and quantify of data is fed to the hungry Private AI applications hiding in the dark shadows of the Internet.

 

Privacy and Security Concerns with leveraging Data for AI

Convenience vs Security

The convenience afforded by OAuth’s integration into AI systems like ChatGPT must be juxtaposed against the backdrop of potential privacy and security concerns.

 

Individual Risks

The primary concern for individuals is the risk of various financial and data hacks as well as identity theft. In an era where data breaches are becoming increasingly common, the centralization of access through OAuth could potentially allow a single point of failure to become the gateway to a user’s digital life. Hackers gaining unauthorized access to an AI service with extensive permissions could manipulate personal information, steal identities, or compromise financial security in a new and comprehensive manner.

 

Corporate Risks

For corporations, the stakes are higher. The integration of AI through OAuth on Public AI could lead to inadvertent data exposure, where sensitive corporate information, including intellectual property, customer data, and internal communications, could be leaked or accessed by unauthorized parties. Such breaches not only have immediate financial repercussions but can also damage a company’s reputation and customer trust, sometimes irreparably.

 

Centralizing Data Enables More Effectivity But Also Increases Risks

Data centralization at 3rd parties presents a tempting target for third parties, including competitors and nation-states, who may exploit security vulnerabilities for espionage or competitive advantage.

 

The implications of such actions can have far-reaching consequences, extending beyond the immediate business context to broader economic and national security concerns.  Data centralized in data centers of even well intentioned, but murky actors becomes a problem if they don’t protect their customers data well enough. There are reports of new hacks at reputable AI vendors daily, it’s commonplace, and worthy of concern.

 

Obvious Legal and Compliance Risks

Additionally, corporations must consider the legal and compliance risks. Data governance and regulatory compliance, such as GDPR in Europe or CCPA in California, mandate strict controls over personal data. A breach resulting from shared AI data could result in substantial fines and legal actions, not to mention the less tangible but equally significant cost of losing customer trust and business.

 

Corporate Dilemmas with Public AI

 

As corporations increasingly consider adopting Public AI systems with OAuth and file-sync and uploads to improve productivity and data access, they encounter a complex web of dilemmas centered around data security and the potential for inadvertent exposure. The use of public AI services, which are more vulnerable to data leaks and accidental exposures vs internal corporate data, requires a careful assessment of the risks and a reevaluation of the cost-benefit paradigm.

 

Public AI (application) providers typically prioritize innovation over security concerns, it takes time, energy and focus provide corporate-grade assurances and compliance.

 

Corporate Concerns for Implementing AI Applications:

Unauthorized Access

Integrating Public AI with corporate data introduces the risk of unintended access, where the AI system could learn from your intellectual property (IP) and sensitive information. This raises concerns about the unpredictability of data flow and usage, including the potential for competitors to access or misuse this information. There’s a lack of control and transparency regarding where the data is disseminated and how it is utilized outside the organization, posing a significant threat to corporate confidentiality and competitive advantage.

Data Leaks

Data leaks are a paramount concern. When corporate data is processed through public AI systems, there’s a risk that proprietary information could be exposed or stolen by bad actors due to security flaws or misconfigurations. This not only compromises competitive advantage but can also lead to legal consequences if customer data or protected information is involved.

The use of public AI platforms could result in data being stored in locations that contravene corporate data residency policies or regulatory compliance requirements.

Accidental Exposures

Accidental exposures represent another significant risk. Employees may, through a lack of understanding of OAuth’s scope or through purposeful or accidental uploading or data-sync, grant more permissions to AI systems than necessary, unwittingly exposing sensitive data. Mismanagement of access rights can lead to scenarios where confidential information is accessible by unintended parties, both internally and externally.

Unpredictable Costs

Beyond data security, there are the unknown costs associated with using public AI platforms. While the upfront costs may seem clear, the total cost of ownership can be obscured by factors such as the long-term implications of data breaches, potential regulatory fines, and the reputational damage associated with security incidents. Furthermore, corporations must consider the costs related to the ongoing monitoring, management, and auditing required to ensure that AI interactions remain secure and compliant.

Unwanted Lock-in to Private AI solutions

There is also the risk of vendor lock-in with Public AI lock-in. Dependence on a particular AI provider’s ecosystem can limit flexibility and bargaining power, potentially leading to unforeseen costs if the provider changes pricing structures, updates access policies, or discontinues services. This reliance can become a strategic vulnerability, especially if the corporation’s critical operations and data sets are tightly coupled with the particular external Public AI service.

 

Enter Private AI – A Corporate Security Shield

 

In response to the security challenges posed by public AI, Private AI presents itself as an advantageous and compelling alternative for companies aiming to maintain data privacy and sovereignty.

Private AI a Safer Option

Private AI refers to AI systems that are developed, hosting data, and operated within a company’s own infrastructure or private secure cloud, rather than uploading or connecting data and user authentications of internal services to external Public AI services (like OpenAI).

The core benefit of Private AI is the control it affords a company over data and Quality of Service while still delivering on innovation and utility AI generally delivers. By keeping sensitive datasets within the corporate realm, companies can significantly reduce the risk of data leaks, unauthorized access, and various potential copyright issues.

This approach aligns with stringent data governance policies and regulatory compliance requirements, ensuring that data does not leave the secure perimeter of the company controlled environment.

Self-Hosting Private Large Language Models (LLMs) Offers Advantages Without Compromise:

Data Sovereignty

Companies retain full ownership and control over their data, which is crucial for sensitive or proprietary information. This means that data handling adheres to the company’s internal policies and complies with jurisdictional privacy laws.

Enhanced Privacy

By operating on private infrastructure, the data used to train and run AI models remains confidential. This is particularly important for industries that handle sensitive information, such as healthcare, finance, and legal services.

Predictable Cost Control

While setting up Private AI requires upfront investment; over time it can offer more predictable and controllable costs compared to public AI services. Companies can avoid unexpected fees, such as those for data ingress and egress, and are not subject to price changes from AI service providers.

Customization and Flexibility

Private AI empowers companies to craft AI models and infrastructure tailored precisely to their unique business requirements, free from the limitations imposed by public AI platforms. This customization paves the way for AI solutions that are finely tuned and exceptionally effective.

With the ability to train the Language Model (LLM) on their proprietary data and seamlessly integrate internal databases, companies can create vastly more potent models. The result is a superior, more closely aligned output. Whether your primary concern is enhanced quality or fostering innovation, there’s a compelling rationale to embrace this route.

Data Security

Companies can apply their own standard security measures and protocols to protect against breaches and ensure that only authorized personnel have access to AI systems and corporate data, as it should be.

Challenges With Enterprise AI Adoption

While Private AI offers numerous advantages, its adoption presents certain challenges. These hurdles encompass the usefulness for in-house or contracted expertise, infrastructure expenses, and the understanding of constructing and sustaining Generative AI models.

Nevertheless, for many companies, these trade-offs are worthwhile due to the increased control and leverage of internal data, reduced exposure to external vulnerabilities, and the more innovative and focused results compared to external solutions.

By embracing Private AI, companies not only fortify their defenses against the risks associated with integrating (new) Public AI but also invest in a strategic asset tailored to their unique operational requirements. This investment can provide a competitive edge, setting them apart from competitors who rely on common tools and take a less relevant data to feed the AI.

Private AI in Practice

Leveraging Private AI, specifically through pre-trained or customized Large Language Models (LLMs), empowers companies to securely expand their focused AI capabilities while maintaining transparency and predictable cost structures.

These versatile and scalable models can be finely tuned using proprietary data and additional internal content and data connected via Retrieval Augmented Generation (RAG), all without the risks associated with public systems and unauthorized third-party data access.

With this Private AI approach, companies can adhere to the same stringent data protection policies that safeguard all sensitive corporate data. They no longer need to place trust in external proprietary AI application systems, ensuring full control over training, fine-tuning, and the connection of selected corporate data sets for utilization.

In the future, it’s easy to image that most corporate and departmental data will be connected or encapsulated in LLMs so humans as well as machines can extract more value from historical and real-time data.

AI Deployment Isn’t Rocket Science

In the past, AI technology deployment was primarily within the realm of specialized AI SaaS companies or large multinational corporations.

NVIDIA’s consistent software advancements have recently democratized AI technology effectively, making it accessible even to organizations lacking extensive prior expertise.

NVIDIA’s AI Enterprise software suite serves as a catalyst for Generative AI, providing a wide array of pre-trained models and integrated workflows tailored for language, text, biotechnology and visual applications. These workflows simplify the process of deploying and customizing AI models, offering tools and simplified workflows for in-house data model training and fine-tuning.

NVIDIA’s AI Enterprise Software Suite is a comprehensive catalog encompassing advanced features such as model training, fine-tuning, retrieval-augmentation, and Inference.

AI Enterprise Software Suite also includes well-known holistic frameworks like NeMo for speech and natural language processing and Picasso for visualization and exploratory data analysis. These capabilities grant users full control over Large Language Models (LLMs) and foundational models, streamlining their interface management.

Strategic Partners Supporting Large Scale Private AI Solutions:

An excellent example of this symbiotic approach to deploy Private AI can be seen in the collaboration between NEBUL, NVIDIA, Dell Technologies and VAST Data, partnering to provide Private AI solutions for Nebul’s Private AI customers.

Together, they provide comprehensive solutions that encompass hardware elements like GPUs, hosting, networking, data storage, and a full suite of software capabilities, similar to what’s deployed by SaaS companies and Hyperscalers, but in a more secure manner where Private AI and data sovereignty come into play.

Nebul’s ‘Powered by NVIDIA’ program caters to a wide range of enterprise-level AI objectives. While some background knowledge in general computing infrastructure, data science, and development is still necessary, these technological advancements have significantly reduced the barriers to entry.

The growing popularity and ongoing advancements in AI technology are making complex AI deployments more achievable and user-friendly. This facilitates the continuous advancement of Private AI innovation while avoiding the potential pitfalls associated with external Public AI providers.

Some of the largest and most successful Private AI projects are designed and deployed by Nebul’s mentioned partners, as they continue to carve a path for Enterprise-Grade Private AI adoption.

The Implications Of Connecting Your Organizational and Personal Data to Public AI

The integration of OAuth into AI services like ChatGPT has far-reaching implications for individuals, particularly as they contemplate the extent to which they intertwine their personal digital footprints with AI capabilities.

The decision to link one’s personal data and applications – emails, calendars, social networks, file stores to AI via OAuth is not just a matter of convenience, but also a significant privacy consideration if the employee mixes personal and professional life, which is commonplace.

The porous boundaries between personal and corporate data pose risks for the company. The convenience of OAuth could potentially lead employees to inadvertently integrate corporate data into public AI systems. This could occur when using personal devices for work purposes (BYOD), which has become commonplace in the modern workplace. Such actions, even if well-intentioned, can result in unintended data leaks and security breaches, exposing sensitive corporate information to third-party AI providers.

In light of these considerations, individuals and employees should consider adopting a cautious approach when integrating AI into their personal and work lives. Organizations can mitigate these risks by establishing clear policies regarding the use of OAuth with AI services, conducting regular security training, and implementing measures to monitor and control the flow of data between personal and corporate environments.

The convenience offered by AI integration into our personal and digital lives is undeniable, but it must be balanced with an awareness of the privacy implications and a commitment to data security to safeguard both individual and corporate privacy.

Risks like identity theft, being locked out of your own digital life, financial loss and unwanted complications are easy to imagine and are already happening.

Unfortunately, bad-actors are also taking advantage of AI’s capabilities, and it means cyber-attacks are that much more efficient, gathering all your personal info and executing damaging attacks automatically. Be careful before sharing your personal data or accounts with Public AI, though not to ignore the productivity and benefits.

Policy and Pricing of Public AI – The Hidden Risks

As organizations integrate AI services into their operations, they must navigate the often-uncertain waters of policy and pricing, which can carry hidden risks.

Public AI application providers, like any business, can (and do) periodically change their terms of service and pricing structures, potentially imposing unanticipated costs on companies that have become reliant on their platforms.

Ever Changing AI Policies – An Evolving Space

The unpredictability of policy changes is a significant concern. Public providers like OpenAI may revise their terms of use, potentially altering data privacy agreements or user rights in ways that could compromise corporate data assets. Such policy shifts could necessitate costly legal consultations and rapid strategic adjustments to ensure continued compliance and data security.

Unpredictable Pricing Models and Levels:

Pricing changes represent another critical risk, particularly for services that charge based on API access. Public AI, with its initially attractive pricing models, can lead to a dependency that vendors may later exploit, raising prices once a lock-in is achieved. For corporations, especially those that have integrated these AI services deeply into their processes, this can result in substantial, unforeseen expenses that impact their bottom line.

To safeguard against these risks, both individuals and corporations can employ several strategies:

Diversification of AI Services

If Public AI services are needed, avoid dependence on a single AI provider by using a multi-vendor approach. This can provide leverage in negotiations and ensure continuity of service should one provider change their policies or pricing. If you do depend on a single-vendor, be sure what assurances and protections you’re offered.

Contractual Safeguards

When entering agreements with AI service providers, negotiate terms that include price and policy stability guarantees for extended periods or at least ensure that there are clear exit strategies should the provider’s changes be unacceptable.

Regular Policy Review

Establish a routine review process to monitor for any changes in terms and conditions of service. This can help organizations to react swiftly to any potential issues that such changes might bring.

Investment in Private AI

Investing and deploying ONLY Private AI solutions will provide more control over both costs and data policies, reducing the risk of exposure to the whims of external service providers, bringing AI application use under the strictest possible policies, usually without stifling the rapid innovation, and in-fact giving better results as AI can access all internal data, improving results and effectivity.

Legal Expertise

Maintain access to legal counsel knowledgeable in technology and intellectual property law to navigate contract complexities and protect the company’s interests. Companies can be caught off guard to find out their data is being mixed with copyrighted data and can be held liable, these issues are still not yet fully addressed. Using your own Private AI (LLM/models) will avoid any such issues entirely.

No Decision is a Decision – Shadow AI is Real

The decision (or lack of a decision) to entrust personal and corporate data to AI services represents a pivotal crossroads – where convenience and innovation collide with data security and privacy.

Balancing Risk and Reward

At the heart of the decision-making process lies the crucial task of balancing risk and reward. Private AI integration, powered by authentication protocols like OAuth and sharing files with AI, promises simplicity and efficiency, personalized experiences, and potential long-term benefits.

However, these advantages must be weighed against the obvious risks, including data breaches, accidental exposures, and potential policy and pricing changes discussed in this article.

To strike this balance effectively, individuals and organizations should consider the following frameworks and policies, usually overlapping with existing corporate standards:

Data Classification

Begin by categorizing data into tiers of sensitivity, ranging from non-sensitive to highly confidential. This classification helps determine which data can safely integrate with AI services and which should remain within private AI ecosystems.

Privacy Impact Assessment (PIA)

Conduct thorough assessments to evaluate the privacy implications of AI integration. Identify potential risks, assess their impact, and develop strategies to mitigate them.

Access Control

Implement stringent access controls and permissions management to ensure that employees, and now AI systems only have access to the data they need for their intended tasks, minimizing exposure or unwanted ingestion of data to external sources.

Data Governance Policies

Establish clear policies and guidelines for data sharing, usage, and retention. Ensure alignment with relevant regulations and compliance requirements.

Vendor Evaluation

When considering public AI services, conduct comprehensive due diligence on providers. Assess their history of policy changes, pricing models, and commitments to data security and privacy.

Scenario-Based Planning

Develop contingency plans for potential policy and pricing changes. Determine how your organization would respond to ensure business continuity and data security.

Monitoring and Auditing

Continuously monitor AI interactions and data flows. Implement regular audits to identify and address potential security gaps and policy violations.

The Path Forward

One thing remains abundantly clear: the landscape of Public AI, especially when it comes to entrusting all our data to privately owned AI companies like OpenAI, currently remains a realm of unknown risks.

The integration of OAuth capabilities and file uploads with Public AI services, exemplified by ChatGPT, promises convenience, innovation and efficiency. It hints at a future where AI seamlessly manages our digital lives, assists in corporate operations, and continually evolves to provide personalized experiences. Yet, this promise is not without its caveats.

There are tangible risks associated with this integration – massive-scale data breaches, accidental exposures, and the potential for unforeseen policy and pricing changes by AI service providers. The delicate balance between efficiency and security requires meticulous consideration.

Concrete Action Plan for Successful and Safe AI Utilization

Taking Concrete Action

Establish Objectives and Outcomes for AI Integration

It’s crucial to define your goals and anticipated results with AI before starting. This sets a clear direction for your AI initiatives.

Conduct Preliminary Experiments

Before fully committing, run a small-scale trial with a minimal budget. This helps to establish practical timelines and budgets, and it aligns with the ‘J-Curve’ Principle for understanding new technology integration.

Showing positive results drives the next wave of investment and progress on AI innovations in your company.

Evaluate Data Privacy with AI

Decide if you are comfortable sharing sensitive data with Public AI systems to maximize their benefits, which also linearly increases risks of data theft. Assume any data you share will become part of the public domain, by accident or theft.

Align with Corporate Policies

Understand corporate policies in terms of sharing various data types with Public AI and external applications.

Communicate with your employees about the company’s approach to AI tools, whether it involves external tools like ChatGPT or the development of in-house Large Language Models (LLMs) utilizing corporate data.

Engage Leadership in AI Strategy

As an executive, discuss AI deployment strategies with your executive team and department heads. Treat the integration of Private AI like any new, value-adding application that brings new risks.

Consult AI Experts for Guidance

If uncertain about AI integration, seek advice from experts (like NEBUL) to explore options and deployment strategies for Private AI and related infrastructure software and cultural shifts needed for success.

Solicit Input from Employees

Engage with your staff and stakeholders to understand their needs and hear their suggestions. They often have insightful ideas and experience on enhancing efficiency and productivity with new tools, and what’s needed to drive innovation and efficiency.

Mitigate Risks with Public AI

Ensure that both the company and employees are aware of the risks associated with connecting sensitive data and apps to Public AI. Develop a roadmap and maintain open communication to learn and adapt quickly. Create Private AI playgrounds for employees to test and give feedback along the way, with security measures and guidelines in place.

Prevent ‘Shadow AI’ and Data Leaks

Clarify AI adoption plans with employees to proactively prevent unauthorized use of external LLMs, which could lead to inadvertent or purposeful data sharing and security risks. Many staff are simply not aware of the risks, and want to do the right thing.

Leverage AI as an Engagement Tool

Use new Public AI tools as an opportunity for meaningful testing and engagement with employees at all levels, building a collaborative plan to maximize benefits. When Public AI tools are used, be sure not to connect any sensitive data, but use more for experimentation and exploration of the possible uses. Let Public AI serve you well, and without unnecessary risks.

Contact Nebul: hello@nebul.com