Integrated Quantum Technologies flagship products veil This allows businesses to protect data before inputting it into ML models using a new privacy protection framework that removes personally identifiable information (PII) while preserving and enhancing the data’s usefulness. VEIL enables scalable innovation without trade-offs and is designed to be future-proof. The threat of quantum computing. The company focuses on enhancing data security, regulatory compliance, and performance of AI/ML systems. Pulse 2.0 interviews Integrated Quantum Technologies CTO Anita Orley To learn more.
Challenges companies currently face
Having spent 25 years transforming enterprise-scale organizations, including leading organizations like AWS Marketplace, what are the biggest challenges facing enterprises today?
“One of the biggest challenges facing enterprises today is how to balance innovation and risk. There is intense pressure to scale AI quickly, but many organizations do not have the right approach to protecting sensitive data.”
“As enterprises continue to rely on machine learning to harden their core systems, traditional security tools are no longer sufficient. Enterprises must use ML models and methodologies designed for modern operating environments. Machine learning models rely on large amounts of data, including sensitive data, to be as effective as possible, making these systems prime targets for attackers.”
How are we addressing these challenges?
How is Integrated Quantum Technologies, where you currently serve as CTO, addressing these challenges? Mr. Oley said:
“Integrated Quantum Technologies is addressing these challenges at the data layer by building security into the foundation of modern AI infrastructure, rather than adding it after the fact. Through our flagship product, VEIL, we transform sensitive data into a secure, non-inverted representation before it enters the machine learning pipeline, enabling effective execution of models with minimal risk.”
“What’s fundamentally different is that we’re not just protecting data in the machine learning lifecycle, we’re also streamlining the use of data. VEIL applies compression techniques that significantly reduce data size while maintaining model accuracy, and in some cases improve model accuracy. In fact, every study we’ve conducted to date has seen improvements in model accuracy. This allows organizations to more efficiently scale their AI/ML solutions and Reduce infrastructure costs and speed time to market.”
“At the same time, VEIL is quantum-resistant by design. Data is irreversible and cannot be reconstructed. This significantly changes the risk profile for organizations working with sensitive data. It allows enterprises to scale machine learning models in a secure and operationally efficient manner without forcing trade-offs between model performance and data exposure.”
Jeremy Samuelson, VP of AI and Innovation at Integrated Quantum Technologies, talks about how VEIL works
Post-quantum and AI security inflection point
Why is now the tipping point for post-quantum security and AI security, and what has changed in the past 12-24 months? Oley pointed out:
“Over the past 12 to 24 months, the inflection point has not been about the adoption of machine learning. Some organizations have been using machine learning for several years. What’s changing is how these models are operationalized at scale. ML As they move from contained, research-driven use cases to operational systems, the amount of sensitive data in active use has increased significantly, as has the number of systems, users, and partners interacting with it. That’s the inflection point. Machine learning hasn’t changed, but the way it’s integrated into business operations and the level of exposure around it has changed.”
Core issues facing businesses today
What are the core problems facing enterprises today that cannot be solved with existing security solutions?
“While enterprises have strong security programs in place, they often lack an approach designed specifically for how data is used within machine learning. What has changed is how important these models have become in driving core business operations and decision-making.”
“This is the core problem: Organizations are relying on methods not designed for this stage of the lifecycle, leaving a gap in how sensitive data is protected in the wild. VEIL solves this problem at the source, protecting data before it enters the machine learning lifecycle.”
Building a post-quantum AI infrastructure
You have described IQT as building a “post-quantum AI infrastructure.” How do you define this category? And why does it need to exist? Ollie explained:
“A ‘post-quantum AI infrastructure’ is about building systems that address today’s machine learning security needs while protecting against future risks associated with advances in computing power, including quantum. Attacks are already being carried out with the most well-known attack called Harvest Now, Decrypt Later, which poses a long-term exposure risk, especially for sensitive data used in machine learning. VEIL is designed with that in mind. Data is transformed into a lossy representation before entering machine learning. Organizations need an infrastructure that protects both how the data is used today and how it can be misused in the future, completely mitigating the risk of future decryption.”
differentiated approach
Is your approach fundamentally different from existing solutions such as encryption, tokenization, differential privacy, etc.? Ory asserted:
“The simplest way to explain this is that existing approaches such as encryption, tokenization, and differential privacy address different parts of the problem. When applied to machine learning, there are also trade-offs. Encryption and tokenization both protect data, but typically require the data to be in a format that can be used by ML models, which introduces the risk of leakage during processing. There are other approaches, such as homomorphic encryption, which can introduce significant performance and scalability challenges.
“VEIL uses a different approach; it transforms data into a lossy representation that protects the data within machine learning pipelines. VEIL avoids the computational overhead and performance trade-offs common with other privacy-preserving techniques, making it lightweight and scalable.”
Enterprise deployment
Where are you currently in terms of readiness for adoption, and what does adoption actually look like in your enterprise? Oley said:
“Our product is commercially available, and we are currently working on pilot implementations with early enterprise partners. VEIL integrates into existing ML environments, making VEIL implementation a reality.”
Feedback from business customers
What are you hearing from your business customers? What’s driving the urgency for them right now? Oley emphasized:
“What we are hearing from enterprise customers today is a growing tension between the desire to scale AI/ML and confidence in how their data is handled. On the one hand, there is increasing pressure from regulators and internal governance teams for control and visibility into how data is used in their models. On the other hand, companies are moving faster and pushing these models into scalable operational systems.”
“Organizations want to do both. They want to be able to innovate quickly and truly operationalize their AI/ML solutions without risking unnecessary data breaches. The stakes are higher now, both from a regulatory and reputational perspective. So the urgency is not just about security in isolation, but about being able to scale machine learning with confidence.”
Position on cybersecurity vendors
How do you position your company vis-a-vis both traditional cybersecurity vendors and emerging AI security companies? Oley pointed out:
“We don’t position ourselves as a cybersecurity solution, and we’re not trying to replace a cybersecurity solution.”
“We operate at the data layer within AI/ML workflows, where data is actively used in pipelines, and that is where the most risk exists today. VEIL is designed to complement existing security tools. We are focused on how data remains protected as it moves from ingestion to training to inference.”
Impact on cost and scalability
How does your technology impact the cost and scalability of AI deployments at the enterprise level? Mr. Oley emphasized:
“This is where customers see immediate value. VEIL compresses and transforms data in the ML lifecycle, which directly impacts cost and scalability. ML infrastructure costs are lower because data sizes are smaller and we can work more efficiently, making it much easier to move data between environments. It also simplifies global deployment.”
“At the same time, organizations can avoid the heavy computing load seen with other approaches and avoid the need to create redundant environments just to manage risk.”
“So it’s not just about mitigating risk, it’s about making AI/ML more efficient and economically scalable.”
Key indicators
For investors evaluating this space, what are the key metrics to pay attention to? Ollie told me:
“Innovations that cannot be safely deployed at scale ultimately create more risk than value. When looking at this space, look not just at where the experimentation and hype is happening, but also where AI/ML That’s where the real challenges lie. It’s also important to pay attention to regulatory pressures, as this will impact how you build and scale your systems. Companies that can address both scalability and security are the ones to watch.”
risk
If companies don’t rethink how they protect their AI infrastructure today, what are the risks going forward? Ory concluded.
“AI increases both opportunity and risk. If the underlying infrastructure is not designed accordingly, small gaps can quickly turn into systemic problems. Over time, trust is eroded, costs increase, and an organization’s ability to scale is limited. Companies that get this right will move faster and more confidently than others.”
