Talk:Data loss prevention software

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Regarding the name of the article

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"Data Loss Prevention" does NOT equal "Data Loss Prevention products". DLP is a concept, not a product or a solution. DLP Products are just that, products. And unless the article is going to list commercial or otherwise, products, then what is "Products" doing in the title? Dekket (talk) 11:41, 29 September 2008 (UTC)[reply]

Regarding Neutrality

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I'm not here to contest whether DLP works, or whether it's necessary, or whether it's a good thing, but I am going to say that all three of those statements are specific points of view. Wikipedia articles must be neutral, which means that you can't describe a product by means of advocacy here.

--- tqbf 04:45, 7 December 2007 (UTC)[reply]


I agree, this article is not very neutral. Terms should be international, not vendor specific. --- de 22:00, 8 January 2008 (UTC)[reply]


I've just read this wikiarticle and was unable to find out which the "neutrality issue" is. Maybe the editing done since the tagging date (Dec'07) has already removed that issue? If so, can this article be un-tagged? Thanks and regards, DPdH (talk) 02:08, 22 February 2008 (UTC)[reply]

In total agreement with the initial neutrality comment. Until a recent edit, this article is clearly skewed toward "content-only" based terms, solutions, and those IT analysts who mistakenly attempt to define the data loss issue to a much-too-narrow scope. The original description/definition completely ignored contextual rules and policies that are crucial to any data security approach. The contextual understanding of "who" (user/group) is doing "what" (copying/printing/"sending") via "which medium" (print/port/device/network transport method) of "which data" (allowed/restricted file types, etc), "when" (during work hours or not), and "to whom" (user/group or outside recipient) are completely relevent to security in prevention of data or information loss. Basing all DLP only on "content", is ignoring this simple reality. 23 November 2009

Vendor presence in this article

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Wikipedia articles get spammed routinely by vendors big and small wanting their company to appear on the first page of Google results for a topic. We had this problem on Talk:Comparison_of_DNS_server_software, and a long-time editor proposed that we restrict vendor coverage to those vendors who already had Wikipedia pages. I disagreed, tried to work with some vendor proponents to make the page work with all vendors, and got burned; many of the "products" covered weren't products, or were no longer being sold, or were clearly not notable.

So, for the health and welfare of this article, I'm a vote for the following editorial stance: vendors can be mentioned here if they have an active DLP line of business and an article in the Wikipedia.

It is not hard to create articles for truly notable DLP vendors; most have tens of press mentions to source an article. Start there first, and then link their Wikipedia page to this article --- not the vendor's website. Per WP:EL, avoid linking to sites whose primary purpose is to sell a product.

--- tqbf 15:28, 10 December 2007 (UTC)[reply]


-- vendors come and go. If you ask me the only reason to have a vendor article in wikipedia is if there's something to criticize about the company that they aren't saying on their own web page. Anything that can be found on the company brochure or by phone call to them is information that doesn't require them to have a wikipedia article. And given that they come and go, no vendor links should be in any pages. Infact you should immediately remove all company links or even mentions whenever you see them. All trademark mentions should also go. (and they do when I see them) —Preceding unsigned comment added by 88.114.203.155 (talk) 00:28, 15 December 2007 (UTC)[reply]

"If you ask me the only reason to have a vendor article in wikipedia is if there's something to criticize about the company that they aren't saying on their own web page." What you just said is very POV. Entbark (talk) 20:00, 24 January 2008 (UTC)[reply]


Agree with the comments. However, after reading this wikiarticle I believe that at this point there is no longer a "vendor presence" in it. If I'm right, can this article be un-tagged? Thanks and regards, DPdH (talk) 02:13, 22 February 2008 (UTC)[reply]

It still reads like an advertisement and has no sources. I added the unreferenced tag. Entbark (talk) 14:46, 22 February 2008 (UTC)[reply]

Recent reversions...

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Hi all, I've just reverted the last 3 changes, all done by the same anonymous user. REASON: what was written looked more an opinion than verifiable facts. Regards, DPdH (talk) 06:17, 15 January 2009 (UTC)[reply]

precise and imprecise methods

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I removed that information because it seems based only on DLP Core Technology. This is the website of a software vendor, claiming that the software that they sell is precise and that all other software is imprecise because it uses "imprecise" methods. It doesn't look like an independent third-party source. Let's see if someone can find a neutral scholar source about the effectivity of data identification methods. --Enric Naval (talk) 01:20, 20 June 2012 (UTC)[reply]

Ugh, the source I added was also sponsored by a vendor, although less directly. --Enric Naval (talk) 14:55, 20 June 2012 (UTC)[reply]

Too "neutral"?

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With no vendor mention/links/presence, the usefulness of this article is reduced. People looking for "Data loss prevention software" would be interested to know about the software solutions out there. The article is just a theoretical discussion of DLP capabilities as it currently stands. At least linking to lists of DLP software would be helpful. 80.254.158.188 (talk) 05:25, 28 November 2013 (UTC)[reply]

Undefined acronym

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"High False Positive Rates will cause the system to be DLD not DLP." DLD is not defined in the article, and I couldn't figure it out by googling. — Preceding unsigned comment added by 207.191.31.200 (talk) 14:14, 29 June 2015 (UTC)[reply]

Doesn't it also include backup software?

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Why is there no mention of backup software in the article yet?

--Fixuture (talk) 14:35, 31 December 2016 (UTC)[reply]

Wiki Education assignment: SSC199 TY2

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This article was the subject of a Wiki Education Foundation-supported course assignment, between 7 November 2022 and 16 December 2022. Further details are available on the course page. Student editor(s): Shonk03 (article contribs).

— Assignment last updated by Shonk03 (talk) 01:12, 16 November 2022 (UTC)[reply]

COI: Proposed updates to modernize technical content and references

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What this article is about: This article concerns data loss prevention (DLP) software, which refers to technologies and processes designed to detect the unauthorized transmission or disclosure of sensitive information and prevent their occurrence.

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

Proposed Edit 1: Update Lead Section Definition

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  • Specific text to be removed:
"Data loss prevention (DLP) software detects potential data breaches/data exfiltration transmissions and prevents them by monitoring, detecting and blocking sensitive data while in use (endpoint actions), in motion (network traffic), and at rest (data storage)."
  • Specific text to be added (replace the above text at the beginning of the article):
"Data loss prevention (DLP) software detects the unauthorized transmission or disclosure of sensitive data and prevents their occurrence, including data in motion (across networks), at rest (in storage), or in use (on endpoints). DLP systems have traditionally relied upon a variety of classification and enforcement mechanisms to reduce the risk of data leakage but increasingly incorporate machine learning and behavioral analytics to enhance detection accuracy. The range of environments in which DLP is used today has widened to include on-premises systems, cloud applications, and hybrid environments."
  • Reason for the change: The existing definition is outdated. The revised version more accurately characterizes the DLP (Data Loss Prevention) technology of 2025, incorporating discussion of machine learning and behavioral analytics, among other things. The original is also unnecessarily technical in parts, whereas the revision offers a more accessible discussion. Moreover, it enriches the discussion with authoritative citations from NIST (2020) and IEEE (2021), lending credibility and depth to the expanded definition.
  • References supporting change:
[1]
[2]

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

Proposed Edit 2: Update "Designated DLP systems" Section

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  • Specific text to be removed (from the "Designated DLP systems" subsection under "Categories"):
"In order to classify certain information as sensitive, these use mechanisms, such as exact data matching, structured data fingerprinting, statistical methods, rule and regular expression matching, published lexicons, conceptual definitions, keywords and contextual information such as the source of the data."
  • Specific text to be added (replace the above text):
"DLP systems commonly use a number of ways to identify sensitive data and classify it accordingly:
* Pattern matching targets structured data formats, such as those typically used for credit card numbers (a 16-digit sequence) or Social Security numbers (the standard, hyphenated 9-digit sequence), which are often represented by 'regular expressions' (RegEx).
* Exact data matching scans for specific, pre-identified sensitive information (e.g., data that matches particular Social Security numbers found in a company database).
* Statistical methods use algorithms based on machine learning classifiers, Bayesian analysis, or support vector machines to identify sensitive data. These rely on probability scores instead of exact matches (e.g., a particular document may be identified as confidential based on term frequency patterns that are similar to those in known classified materials, even when specific keywords have been changed).
* File fingerprinting marks known sensitive documents with unique cryptographic hashes (i.e., digital signatures). Doing so allows systems to detect these files (or substantial portions of them) regardless of filename changes or minor modifications (e.g., the confidentiality of merger document may be maintained even after someone renames it from "Q3_Acquisition.docx" to "Meeting_Notes.docx").
* Conceptual definitions provide a way to identify sensitive data based on meaning and context rather than specific patterns. This approach relies on semantic analysis and natural language processing (e.g., a document may be recognized as containing discussion of sensitive patient information based on the relations between medical terms, procedures, and personal identifiers in the document, even though non-standard terminology or abbreviations are used).
* Keyword and lexicon matching identifies potentially sensitive data by checking for matches against predefined dictionaries of sensitive terms (e.g., flagging documents containing words like "confidential," "proprietary," or industry-specific terms such as drug names in pharmaceutical companies or code names for unreleased products).
Today's DLP systems may also use contextual analysis to account for factors beyond the scope of the document itself being transmitted or modified. These factors may include the user's status and device, as well as the broader transmission flow involved. Machine learning algorithms improve accuracy by identifying subtle patterns and reducing false positives, though classification accuracy remains a persistent challenge as organizations balance strict enforcement with operational usability."
  • Reason for the change: The current text describes classification methods as a run-on sentence that is hard to understand. The revised version suggested instead presents the various methods of data identification/classification as bullet points, each with one or more concrete examples. This makes the technical content more accessible to general readers. It also mentions contemporary methods (contextual analysis, machine learning) with recent citations from IEEE (2017), arXiv (2023), and IBM (2024) to reflect current industry practices.
  • References supporting change:
[3]
[4]
[5]

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

Proposed Edit 3: Expand and Modernize Cloud Section

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  • Specific text to be removed (entire "Cloud" subsection under "Types"):
"The cloud now contains a lot of critical data as organizations transform to cloud-native technologies to accelerate virtual team collaboration. The data floating in the cloud needs to be protected as well since they are susceptible to cyberattacks, accidental leakage and insider threats. Cloud DLP monitors and audits the data, while providing access and usage control of data using policies. It establishes greater end-to-end visibility for all the data stored in the cloud."
  • Specific text to be added (replace the above text):
"Cloud DLP has evolved to meet the security needs arising from the widespread use of Software-as-a-Service (SaaS) and Infrastructure-as-a-Service (IaaS) platforms. Current cloud DLP is deployed in two main forms:
Cloud Access Security Brokers (CASBs) use proxy-based or API-based architectures to monitor data in cloud applications. This allows security policies to be more consistently enforced across disparate platforms.
Cloud-native DLP services from providers such as AWS Macie, Google Cloud DLP API, and Microsoft Purview offer data discovery and protection integrated within their ecosystems. These services use machine learning to automate the identification of sensitive data.
These systems help maintain compatibility with existing on-premises DLP infrastructure while addressing issues that are unique to cloud environments (e.g., shared responsibility models, multi-cloud data governance, and shadow IT discovery)."
  • Reason for the change: The existing cloud section lacks specificity and technical depth. The proposed text provides concrete information about current cloud DLP implementations (CASBs, cloud-native services), names specific solutions from major providers (AWS, Google, Microsoft), and addresses unique cloud security challenges. It includes recent authoritative sources from Forrester (2023), major cloud providers (2024), and NIST (2023).
  • References supporting change:
[6]
[7]
[8]
[9]

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

Proposed Edit 4: Add "Challenges and Limitations" Section

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  • Specific text to be added (new section after "Data in motion" section):
"===Challenges and Limitations===
Current DLP implementation faces a number of important technical and operational challenges:
False positive management remains a significant issue. Policies that are too broad tend to generate alerts that require manual review. This may overwhelm security teams and reduce the overall effectiveness of DLP software.
Privacy and compliance concerns can arise anytime an organization monitors its employee communications. Achieving data security in such situations requires a delicate balance between adequate monitoring and taking care that individual privacy rights are not infringed upon.
Evasion techniques exist (e.g., steganography, encryption, or manipulation of a file's format) that can sometimes circumvent traditional DLP detection methods. This fact underscores the need for continuous updating of detection capabilities.
The complexity of DLP policy increases substantially in global organizations due to their greater size and operation in disparate jurisdictions. DLP software in these cases must often contend with more diverse regulatory requirements, a broader range of data types, and relatively complex business processes. This makes it challenging to achieve consistent enforcement across regions and departments.
To meet challenges of the sorts above, some organizations rely on user and entity behavior analytics (UEBA), insider risk management platforms, and adaptive access controls as complements to traditional DLP."
  • Reason for the change: The article currently lacks balance because it does not address the known limitations of DLP technology. This new section provides important context about real-world implementation challenges (false positives, privacy concerns, evasion techniques, and complexity) that organizations face. The addition enhances article neutrality and comprehensiveness, supported by current sources from 2024 to 2025.
  • References supporting change:
[10]
[11]
[12]
[13]
[14]

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

Proposed Edit 5: Remove Outdated References

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  • Specific text to be removed from References section:
DELETE the following outdated references:
- Reference 1: Hayes, Read (2007)
- Reference 3: Asaf Shabtai, Yuval Elovici, Lior Rokach (2012)
- Reference 4: Phua, C. (2009)
- Reference 6: Ouellet, E., Gartner RAS Core Research (2012)
  • Reason for the change: Removes references from 2007-2012 that no longer reflect current DLP technology and practices. These 13-18 year old sources predate cloud computing adoption, modern machine learning applications, and current regulatory frameworks (GDPR, etc.), making them unsuitable for describing contemporary DLP systems.

Cyberrafael (talk) 20:02, 30 September 2025 (UTC)[reply]

References

  1. ^ "Security and Privacy Controls for Information Systems and Organizations". National Institute of Standards and Technology. 2020.
  2. ^ "A Deep Learning Model for Information Loss Prevention From Multi-Page Digital Documents". IEEE Access. 2021.
  3. ^ "Context-Aware Data Loss Prevention for Cloud Storage Services". IEEE Conference Publication. 2017.
  4. ^ "A Learning oriented DLP System based on Classification Model". arXiv. 2023.
  5. ^ Ponemon Institute (2024). "Cost of a Data Breach Report 2024". IBM Security.
  6. ^ "The Forrester Wave: Data Security Platforms, Q1 2023". Forrester Research. March 2023.
  7. ^ "What is Amazon Macie?". Amazon Web Services. 2024.
  8. ^ "Plan for data loss prevention". Microsoft. 2024.
  9. ^ "NIST SP 800-207A: Zero Trust Architecture for Cloud-Native Applications" (PDF). National Institute of Standards and Technology. September 2023.
  10. ^ "AI in Data Loss Prevention: Safeguarding Sensitive Data Against Unauthorized Access and Leakage". 2024 International Conference on Computer Science and Software Engineering (CSSE). 2024.
  11. ^ "Data Loss Prevention, an EU/GDPR perspective". GRC Outlook. 2024.
  12. ^ "What is Data Loss Prevention (DLP)?". Cyberhaven. 2024.
  13. ^ "2024 Insider Threat Report". Cybersecurity Insiders. 2024.
  14. ^ "IDC MarketScape: Worldwide Data Loss Prevention 2025 Vendor Assessment". IDC. March 2025.