Data security remains a significant challenge for organizations, with the lack of basic data awareness being a major contributor to breaches. According to IBM, 35% of breaches in 2025 involved unmanaged data sources or “shadow data.” This highlights the need for organizations to close the data security maturity gap by embedding protection into their workflows. It’s not just about investing in tooling, but also about understanding what data they have, where it lives, how it moves, and who is responsible for it.
Quick Update: The traditional approach to data security is no longer effective, with perimeter controls and point solutions failing to provide comprehensive visibility and scalability. Can your organization afford to wait and see if its current data security approach will be enough to prevent a breach?
The most persistent barrier to data security maturity is basic visibility. Organizations often focus on the quantity of data they hold, but not on the quality or composition of that data. This lack of understanding makes it difficult to implement meaningful protection. To address this, organizations should prioritize enterprise capabilities that can detect sensitive data at scale across a large and varied footprint. Detection must be paired with action, deleting data where it’s no longer needed, and securing data where it is by aligning enforcement to a well-defined policy.
What are the biggest challenges your organization faces in terms of data security, and how do you think they can be addressed?
How does your organization currently approach data security, and what changes do you think need to be made to improve its effectiveness?
What role do you think automation should play in data security, and how can it be used to improve visibility and control?
Mature organizations should start by treating data security as an “understanding your environment” problem. This involves maintaining an inventory, classifying what’s in the ecosystem, and aligning protections with the classification rather than solely relying on perimeter controls or point solutions to scale. Trend Watch: The use of automation and artificial intelligence in data security is on the rise, with many organizations turning to these technologies to improve their visibility and control. Will your organization be left behind if it doesn’t adopt these technologies?
Data itself is inherently chaotic, making it difficult to secure. Unlike perimeter security, which relies on explicit ports and defined boundaries, data is largely unpredictable. Human behavior compounds the challenge, with different actions introducing risks in ways that perimeter controls simply can’t anticipate. To address this, organizations should embed protection from the moment data is captured, assuming that sensitive data will surface in unexpected places and formats. Defense-in-depth becomes a design principle, with segmentation, encryption at rest and in transit, tokenization, and layered access controls.
New vs. Old: The traditional approach to data security focuses on protecting the network perimeter, while the modern approach embeds protection into the data lifecycle. Which approach do you think is more effective, and why?
The traditional approach to data security, which focuses on perimeter controls and point solutions, is no longer effective. Instead, organizations should adopt a more modern approach that embeds protection into the data lifecycle. This involves using automation to enforce governance, creating bounded contexts, and designing systems that remain secure even when data diverges from expectations.
Quick Update: The benefits of automation in data security are numerous, including improved visibility and control, enhanced scalability and resilience, and reduced risk of human error. Can your organization afford not to invest in automation and risk being left behind?
Data security becomes operationally sustainable when governance is enforced through automation from its genesis. When coupled with clear expectations to create bounded contexts, teams understand what is permitted, under what conditions, and with what safeguards. This approach enables organizations to scale their data security efforts, ensuring that protection is consistent and effective across the enterprise.
FAQ Section:
- Q: What is the biggest challenge organizations face in terms of data security?
A: The biggest challenge organizations face is the lack of basic data awareness, which makes it difficult to implement meaningful protection. This can be addressed by prioritizing enterprise capabilities that can detect sensitive data at scale across a large and varied footprint. - Q: How can organizations improve their data security posture?
A: Organizations can improve their data security posture by embedding protection into their workflows, treating data security as an “understanding your environment” problem, and using automation to enforce governance. - Q: What is the difference between perimeter security and embedded protection?
A: Perimeter security focuses on protecting the network perimeter, while embedded protection integrates security into the data lifecycle. Embedded protection provides comprehensive visibility, scalability, and resilience, making it a more effective approach. - Q: How can automation be used to improve data security?
A: Automation can be used to improve data security by enforcing governance, creating bounded contexts, and designing systems that remain secure even when data diverges from expectations. Automation can also improve visibility and control, reduce the risk of human error, and enhance scalability and resilience. - Q: What are the benefits of adopting a modern approach to data security?
A: The benefits of adopting a modern approach to data security include improved visibility and control, enhanced scalability and resilience, reduced risk of human error, increased efficiency and productivity, and better alignment with regulatory requirements.
