The next constraint on artificial intelligence may not be computing power alone. It may be whether organizations can safely use the data required to make AI useful. As companies seek more recent, accurate and representative information for AI systems, privacy-enhancing technologies investment is becoming relevant to a broader infrastructure question: how can sensitive data be used without unnecessarily exposing it?
The challenge is particularly important because valuable datasets are often fragmented across banks, retailers, telecommunications companies, healthcare providers and other organizations. The World Economic Forum notes that combining these datasets can improve the information available to AI systems, but privacy, security and regulatory constraints can make conventional data sharing difficult.
This creates a potential infrastructure layer between data ownership and AI application: technologies designed to make sensitive information usable while reducing direct exposure.
Why AI’s Data Problem Is Becoming a Privacy Problem
AI systems depend on data, but more data is not necessarily better if organizations cannot lawfully, securely or commercially use it.
Financial institutions hold information about transactions and repayment behavior. Healthcare organizations possess sensitive medical information. Telecommunications companies have extensive information about digital activity. Retailers can hold detailed purchasing patterns. Each dataset can provide useful signals, but combining them can create privacy, security and governance challenges.
The European Union’s AI Act reflects the importance of data quality and governance for high-risk AI systems. Article 10 requires appropriate data-governance practices and addresses issues including data origin, preparation, relevance, representativeness and bias. The Act also recognizes privacy-preserving techniques as part of the safeguards that can be used when sensitive personal data is processed in specific circumstances.
That does not mean regulation guarantees demand or investment returns. It does mean that AI privacy and data governance are becoming part of the broader infrastructure surrounding enterprise AI.
The technologies addressing this problem are not interchangeable.
NIST describes federated learning as an approach that can support AI development across distributed data, while its work on privacy-preserving federated learning also highlights the need to protect model outputs and updates.
Homomorphic encryption takes a different approach. NIST describes it as enabling computation on encrypted data without requiring the data to be decrypted first, although computational cost can be a limitation depending on the application.
The Technologies Building a Privacy Layer for AI
Confidential computing is particularly relevant to cloud-based AI because it addresses data in use. NIST’s 2026 draft report on confidential computing describes mechanisms for protecting data while it is being processed in memory and specifically examines protection of datasets used by AI workloads in cloud environments.
Trusted execution environments are part of this architecture. ENISA describes a TEE as a protected processing environment intended to maintain the confidentiality and integrity of code and data during execution.
Federated learning addresses a different problem. Instead of moving all training data to one central location, participating organizations can train locally and exchange model information. Privacy protections can then be added to reduce what can be inferred from those updates. NIST has documented both privacy attacks against federated-learning systems and techniques such as differential privacy to mitigate information leakage.
Secure multiparty computation and homomorphic encryption can provide another route for collaboration. NIST notes that secure multiparty computation can produce results from multiple databases without requiring the databases themselves to be combined, while homomorphic encryption allows computation over encrypted information.
The economic applications therefore differ. One technology may be most appropriate for protecting cloud workloads, another for distributed machine learning and another for collaborative analytics.
Why Regulated Industries Could Drive Adoption
Financial services, healthcare, insurance, telecommunications and government are natural areas of interest because their operations can involve highly sensitive information.
The commercial logic is straightforward. An organization may possess valuable data but be unable or unwilling to transfer raw records to another organization. PETs can potentially allow specific analysis or model training without requiring the same degree of direct data exchange.
The World Economic Forum recently described a South African example in which a grocery retailer and banks tested the use of grocery-shopping behavior to assess creditworthiness without directly sharing consumer data between the organizations. The system used tokenization and a privacy-preserving environment to combine information for analysis.
The significance is broader than credit scoring. Similar architecture could be relevant wherever organizations have complementary datasets but face constraints around sharing them.
That creates a possible chain:
Data Collaboration → AI Development → New Products → Data Monetization
But the final step is not automatic. A technology must solve a sufficiently valuable problem, operate at acceptable performance and cost, integrate into existing systems and provide a commercial reason for customers to pay.
Privacy Could Become Part of the AI Infrastructure Stack
AI infrastructure is often discussed in terms of processors, data centers, cloud platforms and networking. Yet those systems are only useful when organizations can deploy AI against data they are permitted and equipped to use.
The resulting stack increasingly looks interconnected:
Compute + Cloud + Data + Security + Privacy + AI Models
NIST’s 2026 confidential-computing work explicitly connects protection of sensitive data with AI workloads running on cloud infrastructure.
This makes privacy technology potentially more than a compliance expense. In some applications, it can become an enabling layer that allows data to move through an analytical process without requiring organizations to expose the underlying records in the same way as conventional data-sharing arrangements.
For investors, that distinction matters. A standalone privacy product may have a narrow market, while a privacy capability embedded into cloud infrastructure, cybersecurity, data governance or AI platforms could address a broader enterprise requirement.
The Business Model Behind Privacy Technology
The potential commercial models span software subscriptions, cloud services, enterprise contracts, licensing, specialized hardware and security infrastructure.
The investment opportunity therefore extends beyond companies whose primary business is labeled “privacy.”
Cloud providers can incorporate confidential-computing capabilities. Cybersecurity companies can add privacy-preserving controls. Data-governance platforms can integrate PETs into broader compliance and analytics workflows. Specialized hardware can support protected computing environments.
This creates a wider privacy-enhancing technologies investment landscape connected to AI infrastructure and cybersecurity rather than a single product category.
The important distinction is between technological importance and monetization. A technically impressive system may still struggle if implementation is difficult, customers are unwilling to pay or competing approaches provide a simpler solution.
The Investment Market for Privacy-Enhancing Technologies
For investors, PETs can therefore be examined across several parts of the technology stack.
| Investment Theme | Potential Opportunity | Key Risk |
|---|---|---|
| PET software | Enterprise privacy-preserving analytics and AI | Adoption and integration costs |
| Confidential computing | Protected cloud and AI workloads | Hardware and platform dependence |
| Encryption technology | Secure processing and data collaboration | Performance and complexity |
| AI security | Protection of models, data and inference | Rapidly changing threat landscape |
| Cloud infrastructure | Embedding privacy into enterprise AI services | Competition and customer concentration |
| Data governance | Managing compliant and controlled AI data use | Regulatory and integration complexity |
| Specialized hardware | Hardware-enabled trusted environments | Capital intensity and technological change |
This is why privacy-enhancing technologies investment should be evaluated as a broader AI infrastructure theme rather than simply a bet on specialist privacy companies.
Investors should examine technology maturity, enterprise demand, performance, recurring revenue potential, customer concentration, integration costs and competitive position. Regulatory drivers may influence demand, but they do not determine profitability.
The Risks Behind the PET Investment Thesis
The biggest obstacle is that privacy can be strategically valuable while remaining difficult to monetize.
NIST has identified scalability and performance challenges in privacy-preserving federated learning. Cryptographic techniques such as homomorphic encryption and multiparty computation can introduce computational overhead, while systems may also need to accommodate organizations with different technical capabilities.
Other barriers include implementation costs, limited specialist expertise, interoperability, customer willingness to pay and rapidly changing technology.
There is also no guarantee that a particular PET approach will become a standard. Open-source developments, competing architectures and advances in conventional security could alter the competitive landscape.
For investors, this makes due diligence essential. The central question is not simply whether a technology protects privacy. It is whether customers consider that protection sufficiently valuable to justify recurring expenditure.
Unique Insight
The deeper privacy-enhancing technologies investment thesis is not simply:
“Privacy is becoming more important.”
It is:
AI cannot fully monetize the world’s most valuable data if organizations cannot safely use it.
That creates a new infrastructure problem.
The future AI stack may increasingly look like:
Compute → Models → Data → Security → Privacy → Enterprise Applications
PETs could help organizations move from:
“We possess valuable data but cannot freely share or process it.”
toward:
“We can extract intelligence from sensitive data while reducing direct exposure of that data.”
That makes privacy technology potentially an enabler of economic activity, rather than only a defensive control.
The investment question therefore becomes:
Which privacy technologies can move from technically useful tools into infrastructure that enterprises are willing to pay for at scale?
Conclusion
AI needs data. Data creates privacy risk. Regulation increases accountability. PETs can provide mechanisms for safer data use. Enterprise adoption determines commercial value.
The case for privacy-enhancing technologies investment rests on this intersection.
As AI expands into financial services, healthcare, government, telecommunications and other data-intensive industries, organizations will continue to face the challenge of extracting value from information while maintaining appropriate privacy and security controls. PETs offer several technical approaches to that problem, from confidential computing and trusted execution environments to federated learning, homomorphic encryption, secure multiparty computation and differential privacy.
The key investment question is not simply which privacy technology is most advanced.
It is which technologies can make sensitive data more usable, scalable and economically valuable without undermining the trust and protections that govern it.
Frequently Asked Questions
What are privacy-enhancing technologies?
Privacy-enhancing technologies are technical approaches designed to enable data processing or analysis while reducing exposure of sensitive underlying information. The category includes cryptographic, statistical and hardware-based techniques.
What is privacy-enhancing technologies investment?
Privacy-enhancing technologies investment refers to investment exposure to companies, software, infrastructure or hardware involved in technologies that enable privacy-preserving data processing and collaboration.
How do privacy-enhancing technologies support AI?
They can help organizations analyze, train or process sensitive data while reducing the need to expose or centralize raw information.
What is confidential computing?
Confidential computing uses hardware-enabled mechanisms to protect data while it is being processed, including in cloud environments.
How does federated learning protect data?
Federated learning allows model training across distributed datasets rather than requiring all underlying training data to be centralized. Additional privacy techniques can protect model updates and outputs.
What is homomorphic encryption?
It allows certain computations to be performed on encrypted data without first decrypting it.
Why could healthcare companies adopt privacy-preserving AI?
Healthcare organizations handle highly sensitive information, creating a potential use case for technologies that enable analysis while reducing unnecessary exposure of underlying data.
How does AI regulation affect privacy technology?
AI regulation can increase requirements around data governance, privacy, security and accountability. It can influence technology requirements, but does not guarantee commercial success or investment returns.
Can PETs become part of AI infrastructure?
They could become an important layer of enterprise AI infrastructure where organizations need to process sensitive data under technical, legal or commercial constraints. NIST’s work on confidential computing specifically addresses protection of data used by AI workloads.
What are the risks of investing in privacy technology?
Key risks include technical complexity, performance overhead, cost, integration challenges, uncertain adoption, competition, interoperability and rapid technological change. NIST has documented scalability and performance challenges in privacy-preserving federated learning.
Why are privacy-enhancing technologies important for enterprise AI?
They can potentially allow organizations to use valuable data that might otherwise be difficult to combine or process because of privacy and security constraints.
How can PETs enable secure data collaboration?
Depending on the technology, organizations can perform analysis across distributed datasets, compute on protected information or collaborate without directly exchanging the underlying raw data.
Administrator at Alt Finances, leading editorial strategy and contributing in-depth coverage of investing, wealth management, alternative assets, and global financial markets. Through research-driven articles and analysis, he helps readers understand the ideas, industries, and market forces shaping modern finance.
