Data Loss Prevention (DLP): What It Means in 2026 and Why Traditional Approaches Fall Short
Data loss prevention has been around for years. Most security teams recog...
Data never stands still.
Every second, it’s moving — across devices, clouds, and collaboration tools — while other data quietly lives in storage, waiting to be used again. More than ever, data flows nonstop, whether it’s finance teams uploading reports to Copilot or developers syncing repositories.
That constant movement has made protecting data far more complicated. With GenAI tools now plugged into the same pipelines as employees, every data transfer could expose sensitive information if not properly secured. Understanding where your data lives and how it moves is the first step to defending it.
And as that data travels or settles, it exists in two key states: data in motion and data at rest.
Let’s break down how they differ, why each carries its own risks, and how Concentric AI helps protect both — automatically, across every environment.
What is the difference between data in motion and data at rest?
Think of data as having two speeds. Data in motion is active: it’s being sent, shared, or transmitted between locations. Data at rest is static: sitting in a database, server, or storage drive.
While both are vulnerable, their risks are different.
Data in motion faces interception, corruption, or unauthorized access as it moves between systems or cloud services.
Data at rest is exposed when permissions are misconfigured, files are stored in the wrong place, or sensitive records are left unmonitored for years.
Organizations need to secure both states. Encryption and secure transfer protocols protect moving data, while access controls, monitoring, and classification protect data that’s stored. The real challenge is keeping track of which data is in which state and knowing who can reach it.
GenAI data in motion is the new challenge
Generative AI has added a new layer of data in motion. Every time a user uploads a document into Copilot, ChatGPT, or another GenAI system, sensitive data may flow into external models. And sometimes without the user realizing it.
This new form of data movement doesn’t follow traditional network paths. Instead, it happens through API calls, model training pipelines, and AI-driven workflows. That makes it harder to monitor with legacy security tools. GenAI data in motion is dynamic, often invisible to IT teams, and capable of transmitting sensitive data across boundaries in seconds.
Today, companies need visibility into these flows to identify when confidential data is used, shared, or uploaded to GenAI tools.
What is data in motion?
Data in motion, also known as data in transit, refers to data that is actively being transferred or moved from one location to another. Examples of data in motion include private networks, public internet channels, cloud or SaaS providers, wireless transmissions, and physical methods like portable drives.
Threats
Data in motion is susceptible to many threats, including:
Interception: Data can be intercepted by malicious actors which can cause unauthorized access or a data breach.
Corruption: While in motion, data may become corrupted for various reasons, including network issues, hardware failures, or human error.
Latency: The time it takes for data to move from one point to another can be crucial in real-time applications where a delay can impact business operations.
Security measures
Traditional security measures taken to protect data in motion include:
Encryption: Ensures that even if data is intercepted, it remains unreadable to unauthorized parties.
Secure Channels: Using secure communication channels like VPNs or secure protocols like HTTPS to safeguard data.
Authentication: Implementing robust authentication processes to validate the people or parties involved in data transmission.
Concentric AI is easy to deploy — sign up in ten minutes and see value in days.
What is data at rest?
Data at rest refers to data that is stored in persistent storage, not actively being moved or processed. Examples of data at rest include databases, hard drives, cloud storage, or any other form of static storage. Data at rest can be structured or unstructured.
Threats
Threats to data at rest include:
Unauthorized Access: Stored data can be accessed by unauthorized parties, leading to potential data leaks or breaches. Examples of unauthorized access include inappropriate permissioning, risky sharing, and data stored in the wrong location.
Compliance: Ensuring stored data complies with various data protection regulations like GDPR or CCPA, which mandate strict data protection measures.
Data Degradation: Over time, physical storage media can degrade, leading to potential data loss.
Security measures
Traditional security measures to protect data at rest include:
Access Control: Implementing strict access control policies to ensure only authorized individuals or parties can access the data.
Data Masking: Using data masking to hide specific data within a database, rendering it inaccessible for unauthorized users.
Regular Audits: Conducting regular security audits to identify and remediate any vulnerabilities or misconfigurations.
Protecting data in Healthcare, Finance and Education
Let’s look at a few hypothetical scenarios in the healthcare, finance, and education sectors to shine more light on the importance of comprehensive data protection strategies.
Healthcare
In healthcare, there are numerous types of patient data to protect, ranging from electronic health records (EHRs) to real-time monitoring data of a patients’ vitals. Protecting data in motion becomes crucial when hospitals share patient data with third parties, specialists or when integrating with pharmacies for prescriptions.
If this data is intercepted, corrupted or altered, it can lead to misdiagnosis or incorrect medication dosages. On the other hand, data at rest faces threats from unauthorized access, potentially leading to identity theft or breaches of sensitive health data. For example, a hospital system that experiences a breach as a result of a ransomware email, leading to the exposure of millions of patient records.
Finance
The finance sector is built on trust, with transactions involving sensitive data such as bank account numbers and investment details. Data in motion is critical during online transactions or when transferring information between financial institutions.
If this data is intercepted, it can result in financial loss or fraud. Data at rest in financial institutions must be protected against unauthorized access to prevent identity theft and financial fraud. For example, a bank that experiences a coordinated cyberattack which results in a loss of customer trust and harsh financial penalties.
Education
Educational institutions hold vast amounts of student data, from personal identification information to academic records. Data in motion includes the exchange of this information between departments or with external educational tools and services.
Corruption or interception here could affect students’ academic integrity or privacy. Data at rest needs protection against unauthorized access to prevent identity theft or misuse of intellectual property. For example, an educational institution that is hacked due to inadequate access permissions, leading to the unauthorized access of sensitive student records, which in turn has a negative impact on future student admissions.
Three modern challenges of protecting data at motion and at rest
Over and above the largest modern challenge of widespread GenAI use we discussed above, there are three key hurdles that organizations still face today.
Massive cloud migration
With more data to manage and process than ever before, organizations are navigating the challenges of moving data and applications to the cloud — lured by its scalability, flexibility, and cost-effectiveness.
However, cloud migration is not without its hurdles. Ensuring stringent data security during the migration and subsequent storage in the cloud is crucial given the potential vulnerabilities due to misconfigurations and inadequate access controls. Plus, managing data across hybrid or multi-cloud environments introduces complexities that demand robust data management strategies and adherence to a myriad of regional and industry-specific compliance mandates.
Exponential data growth
Organizations are grappling with the surge in data generation and collection — driven by factors such as IoT devices, social media, and more digital transactions.
The challenges are numerous:
- Ensuring secure and accessible storage for burgeoning data volumes
- Efficiently processing vast amounts of data to extract actionable insights without overwhelming systems
- Protecting the increasing data volumes against unauthorized access and potential breaches
The infrastructure strain to accommodate growing data can impact performance and reliability, while larger data sets become lucrative targets for cybercriminals.
The evolving workspace
Remote and hybrid work models have redefined traditional norms and introduced new dynamics in data access and sharing. Giving employees seamless access to the data they need — whenever they need it and without compromising on security — becomes a never-ending balancing act.
The expansion of the network perimeter to include remote work environments necessitates enhanced network security measures, and policies must be adapted to cater to the new work dynamics while ensuring data security and compliance.
How Semantic Intelligence provides protection for data, regardless of its state
Here are 5 ways Concentric AI can help with protecting data – no matter what state it’s in.
- Autonomous data discovery
It may be cliche, but it’s true: identifying where sensitive data resides is like finding a needle in a haystack.
Concentric AI’s Autonomous Data Discovery uses advanced machine learning to scan, identify, and categorize data without human intervention. Concentric AI can handle vast amounts of data, making it suitable for organizations of all sizes. Best of all, organizations can reduce the time and resources required for manual data categorization and discovery.
2. Risk Distance™ Analysis
Traditional security measures rely on predefined rules, which can quickly become outdated and miss new threats. Concentric AI’s Risk Distance™ Analysis uses deep learning to compare each data element with established security practices, identifying deviations and potential risks while reducing false positives.
Our solution continuously updates its understanding based on new data and threats — putting organizations in a better position to identify risks before they escalate into breaches.
3. Continuous risk monitoring
Concentric AI offers continuous risk monitoring, ensuring real-time detection of risks from inappropriate permissions, risky sharing, and unauthorized access.
Concentric AI monitors all data access points, from databases to cloud storage, structured and unstructured, providing a clear overview of data access patterns and helping identify potential vulnerabilities.
4. Regulatory compliance
With a myriad of data protection regulations being introduced for numerous industries, countries and jurisdictions, maintaining compliance can be daunting. Concentric AI helps businesses navigate this complex landscape, offering tools and insights to meet global mandates.
Our solution provides detailed logs and reports, which is crucial for simplifying the audit process and helping with compliance efforts. Organizations can proactively detect potential compliance violations, allowing for timely remediation.
5. Easy implementation
Today’s businesses require solutions that integrate seamlessly without disrupting existing workflows. Concentric AI is agentless, API-based, and compatible with various data repositories. Our solution integrates with a wide range of data storage solutions, from on-premises databases to cloud platforms — reducing the time-to-value and providing immediate benefits.
Want to see firsthand — with your own data — how you can quickly and easily deploy Concentric AI’s solution to leverage the power of AI to protect data no matter what state it’s in? Book a demo today and experience the freedom of protecting your data without rules, regex, or end-user involvement.