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The Role of Emerging Technologies in Enterprise Strategy

Jacek Chmiel
Avenga

Enterprise privacy is viewed more as a challenge and bottleneck in adopting AI and cloud API-driven projects than an opportunity to lower the risk of image and money loss thanks to novel technological solutions. There's a lot of focus on regulatory paperwork, and declarative formal privacy. Departments were created, roles were set, people were assigned, and privacy policies were written and published. Cookie warnings are implemented on corporate websites and mobile apps, etc. So from the basic regulatory obligations, all the requirements are met and fingers crossed that incoming regulations will not require too much additional effort.

However, behind the closed doors of decision-makers, real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations.

This is a missed opportunity because true privacy protection can be achieved with the right set of processes and technologies without killing the budget. The strategy of deprioritization and effort minimization unfortunately means that many technology teams are not even aware of available privacy protection options and progress in the field.

No Privacy without Security

First, there's no privacy without security, we need to connect both instead of separating them as issues because they are heavily interdependent. The security landscape is demanding more attention than ever, and getting more complex. We observe increased sophistication of cyberattacks on both social and technological levels. It doesn't mean that security and privacy improvements are blocking each other, they can and should be progressing in parallel, but understanding mutual dependencies is the key to the success for both.

Basic rules and fundamentals of security, are not that different from those twenty years ago. Memory attack vulnerabilities are still dominating, as most of the system-level software is still written in unsafe C and C derivatives. Browsers suffer from client-side attacks targeting rendering and JavaScript engines. The thing that changes the most is the pace of change due to automation and the rise of state-sponsored hacking groups targeting competitors and enemies in trade and hybrid cyber warfare.

AI-driven attackers are much more successful and automated than they used to be. AI-supported defenders also get new tools, and more advanced scanners at the source code, container, and runtime levels. However, the defense seems to be at a comparative disadvantage.

The fundamentals of security change slowly, which unfortunately does not mean they are perfectly implemented. Daily reports of data leaks typically comes down to the usual set of mishaps on the data owners and processing sites.

Data Needs to Be Protected

Decades-old strategies such as data minimization are still not a norm, as even simple transactions require users to provide too much information than is necessary to perform the transactions. The pressure to know and target customers better allow companies to get tons of telemetric data, bordering or crossing privacy violation borders of individuals and organizations.

Avoiding data copies is another recommendation that is true today and is not going away anytime soon. Unfortunately, data is copied unnecessarily which makes data retention policies much harder, and allows for a much larger data attack surface, without proper processes and tools. This also includes the rise of observability in distributed systems, which can create copies of sensitive data in the system logs that are hard to detect and remove.

Data retention conformance still relies too much on declarative statements than the physical destruction of data, for instance, already trained machine learning models make it almost impossible and it's a secret that everyone in the AI industry knows well. The models would have to be retrained with specific source data removed which is very costly and time-consuming. And that's on top of existing issues of backups and logs stored for years in digital archives.

Privacy of public LLM services has also been proven questionable, as uploaded corporate data containing business secrets as well as sensitive personal data are then used for model training. Free public services used by corporate users (instead o or besides corporate chatbots) often come with a hidden privacy "price," as the saying goes "if it appears to be free, you are the product."

Language models and machine learning models in general exhibit a memorization problem that leaks sensitive data with malicious prompting. Local Small Language Models (SLM) combined with Retrieval Augmented Generation (RAGs) are alternative that balances model performance with guarantees that no documents will be used to train global models. Another option is to rely on signed agreements with big tech companies and rely on their declarations of not using corporate data for model training.

Multi-device usage and bring-your-device (BYOD) trends mean that mobile applications requesting too many permissions to track users may contribute negatively to the privacy exposing data of employees, customers, and patients. There are corporate policies that reduce the exposure of corporate data, they need to be implemented correctly, however, devices do increase the attack surface and risks.

PET to the Rescue

Privacy Enhancing Technologies such as Differential Privacy (DP) are already mature enough to be used in real-world applications. They practically mean a slight deterioration of data usability but make it much harder to identify individuals. The tradeoff between privacy and data value should be considered every time there's a risk of data exposure, with a slight addition of controllable noise we can gain a much higher protection of data and machine learning models against membership inference attacks.

The federated learning strategy helps to train models without moving or accessing any sensitive data, avoiding data copy (and data retention) problems altogether. Data sharing agreements do not need to be signed, as there is no data sharing, of course, the operations performed must be non-disclosive, the models cannot be overtrained and assumed 100% private, so there's room for secure aggregation and differential privacy.

The space of PET is maturing fast, it's underestimated but it could dramatically improve privacy protection when combined with enterprise security and privacy-preserving mindset.

Confidential Computing

Another emerging technology is confidential computing which protects companies and their data against malicious administrations of hosting and cloud services. No one except people who are allowed from the company can access data at any time as it is always kept encrypted and the cloud provider does not have the keys. The technologies are improving fast, and there are already practical applications. The near-term future is expected to deliver on the promise of CPU and GPU accelerated workloads, very important for the entire machine learning field, including generative AI.

Future

The pressure on privacy put by the regulators is only going to increase. Fortunately, there's an entire ecosystem of strategies, technologies, and tools to help to minimize the risks without significantly affecting the IT budgets. Federated networks of (limited) trust are growing, privacy enhancing techniques deliver better results at a lower cost of data value. Confidential computing is improving fast and slowly gaining traction. In the times of generative AI news thrown at us daily it's easy to overlook the significant technological progress in this area of privacy protection due to technological advances. When combined with the right process and, most importantly, a privacy-preserving attitude of individuals and entire organizations, privacy can become a part of competitive advantage, significantly lowering risks of image and reputation damage and money loss due to regulation violations.

Jacek Chmiel is Director of Avenga Labs

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Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...

The Role of Emerging Technologies in Enterprise Strategy

Jacek Chmiel
Avenga

Enterprise privacy is viewed more as a challenge and bottleneck in adopting AI and cloud API-driven projects than an opportunity to lower the risk of image and money loss thanks to novel technological solutions. There's a lot of focus on regulatory paperwork, and declarative formal privacy. Departments were created, roles were set, people were assigned, and privacy policies were written and published. Cookie warnings are implemented on corporate websites and mobile apps, etc. So from the basic regulatory obligations, all the requirements are met and fingers crossed that incoming regulations will not require too much additional effort.

However, behind the closed doors of decision-makers, real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations.

This is a missed opportunity because true privacy protection can be achieved with the right set of processes and technologies without killing the budget. The strategy of deprioritization and effort minimization unfortunately means that many technology teams are not even aware of available privacy protection options and progress in the field.

No Privacy without Security

First, there's no privacy without security, we need to connect both instead of separating them as issues because they are heavily interdependent. The security landscape is demanding more attention than ever, and getting more complex. We observe increased sophistication of cyberattacks on both social and technological levels. It doesn't mean that security and privacy improvements are blocking each other, they can and should be progressing in parallel, but understanding mutual dependencies is the key to the success for both.

Basic rules and fundamentals of security, are not that different from those twenty years ago. Memory attack vulnerabilities are still dominating, as most of the system-level software is still written in unsafe C and C derivatives. Browsers suffer from client-side attacks targeting rendering and JavaScript engines. The thing that changes the most is the pace of change due to automation and the rise of state-sponsored hacking groups targeting competitors and enemies in trade and hybrid cyber warfare.

AI-driven attackers are much more successful and automated than they used to be. AI-supported defenders also get new tools, and more advanced scanners at the source code, container, and runtime levels. However, the defense seems to be at a comparative disadvantage.

The fundamentals of security change slowly, which unfortunately does not mean they are perfectly implemented. Daily reports of data leaks typically comes down to the usual set of mishaps on the data owners and processing sites.

Data Needs to Be Protected

Decades-old strategies such as data minimization are still not a norm, as even simple transactions require users to provide too much information than is necessary to perform the transactions. The pressure to know and target customers better allow companies to get tons of telemetric data, bordering or crossing privacy violation borders of individuals and organizations.

Avoiding data copies is another recommendation that is true today and is not going away anytime soon. Unfortunately, data is copied unnecessarily which makes data retention policies much harder, and allows for a much larger data attack surface, without proper processes and tools. This also includes the rise of observability in distributed systems, which can create copies of sensitive data in the system logs that are hard to detect and remove.

Data retention conformance still relies too much on declarative statements than the physical destruction of data, for instance, already trained machine learning models make it almost impossible and it's a secret that everyone in the AI industry knows well. The models would have to be retrained with specific source data removed which is very costly and time-consuming. And that's on top of existing issues of backups and logs stored for years in digital archives.

Privacy of public LLM services has also been proven questionable, as uploaded corporate data containing business secrets as well as sensitive personal data are then used for model training. Free public services used by corporate users (instead o or besides corporate chatbots) often come with a hidden privacy "price," as the saying goes "if it appears to be free, you are the product."

Language models and machine learning models in general exhibit a memorization problem that leaks sensitive data with malicious prompting. Local Small Language Models (SLM) combined with Retrieval Augmented Generation (RAGs) are alternative that balances model performance with guarantees that no documents will be used to train global models. Another option is to rely on signed agreements with big tech companies and rely on their declarations of not using corporate data for model training.

Multi-device usage and bring-your-device (BYOD) trends mean that mobile applications requesting too many permissions to track users may contribute negatively to the privacy exposing data of employees, customers, and patients. There are corporate policies that reduce the exposure of corporate data, they need to be implemented correctly, however, devices do increase the attack surface and risks.

PET to the Rescue

Privacy Enhancing Technologies such as Differential Privacy (DP) are already mature enough to be used in real-world applications. They practically mean a slight deterioration of data usability but make it much harder to identify individuals. The tradeoff between privacy and data value should be considered every time there's a risk of data exposure, with a slight addition of controllable noise we can gain a much higher protection of data and machine learning models against membership inference attacks.

The federated learning strategy helps to train models without moving or accessing any sensitive data, avoiding data copy (and data retention) problems altogether. Data sharing agreements do not need to be signed, as there is no data sharing, of course, the operations performed must be non-disclosive, the models cannot be overtrained and assumed 100% private, so there's room for secure aggregation and differential privacy.

The space of PET is maturing fast, it's underestimated but it could dramatically improve privacy protection when combined with enterprise security and privacy-preserving mindset.

Confidential Computing

Another emerging technology is confidential computing which protects companies and their data against malicious administrations of hosting and cloud services. No one except people who are allowed from the company can access data at any time as it is always kept encrypted and the cloud provider does not have the keys. The technologies are improving fast, and there are already practical applications. The near-term future is expected to deliver on the promise of CPU and GPU accelerated workloads, very important for the entire machine learning field, including generative AI.

Future

The pressure on privacy put by the regulators is only going to increase. Fortunately, there's an entire ecosystem of strategies, technologies, and tools to help to minimize the risks without significantly affecting the IT budgets. Federated networks of (limited) trust are growing, privacy enhancing techniques deliver better results at a lower cost of data value. Confidential computing is improving fast and slowly gaining traction. In the times of generative AI news thrown at us daily it's easy to overlook the significant technological progress in this area of privacy protection due to technological advances. When combined with the right process and, most importantly, a privacy-preserving attitude of individuals and entire organizations, privacy can become a part of competitive advantage, significantly lowering risks of image and reputation damage and money loss due to regulation violations.

Jacek Chmiel is Director of Avenga Labs

Hot Topics

The Latest

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty

 

Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...