Observations
Data protection is non-negotiable. In fact, private sector spending on data security exceeds $100B annually. Companies often rely on Data Loss Prevention (DLP) solutions to stop sensitive information from leaking outside their walls.
Yet, the limitations of traditional DLP are becoming evident, leaving organizations exposed to risk they don’t see coming, especially as the use and adoption of AI accelerates. That’s where AI-based next-generation DLP comes into play. It’s a smarter layer of protection that closes the gaps traditional solutions miss.
In this blog, we’re going to walk through a real-world example to illustrate the difference in the risk a traditional DLP will surface versus the risk even a human might not detect but a next-gen DLP will.
Example: A Single Email, a Multitude of Risk
Emails continue to be a standard form of communication within and between businesses. The DLP solution you’ve had in place for a while is most likely overseeing this form of communication and you may feel like it’s doing a good enough job. But let’s take a look at the potential risk to your organization that could exist in a single email written by an employee in an organization that uses an AI tool to help him write it.
The email is written by Raul Ramirez, a relationship manager at Smooth Bank, Inc., who sends the following email to a customer. Smooth Bank does allow the use of AI to help its employees become more efficient, but is using traditional DLP to oversee any potential risks. Raul did use Copilot to help him create the email and it’s pulling its inputs from various Smooth Bank systems.
Can you spot all the potential risks in this email? 
Traditional DLP in Action
Traditional DLP solutions typically focus on static patterns such as keywords, regular expressions, or rule-based signals for sensitive information like credit card numbers, social security numbers, or standard personal identifiable information (PII).
In this email, traditional DLP would capture the content highlighted below in purple as these resemble PII because they are a string of numbers of a certain length. These are straightforward, matching set rules or pre-defined templates. But this approach misses the forest for the trees, only spotting the “obvious” risks, while ignoring broader and subtler threats. 
Using AI-Driven Next-Gen DLP: Seeing the Whole Picture
Applying an AI-driven next-gen DLP will use AI, entity-awareness, and deep context analysis, going beyond pattern matching to uncover advanced risks. Let’s review what it would find in the same scenario.
#1 – Recipient Anomaly & Dollar Amount
The email cc field, highlighted in blue, shows the recipient to be “john.petrov” but the body of the email shows a John Smirnov, the actual intended recipient.
Mismatch: Because these two entities do not match, we have a signal that there is a possible misdirected email, one of the most common and costly data leakage vectors.
Unauthorized Financial Leakage: The “$412,90,114” dollar amount is disclosed to an external party. AI context cues recognize this as privileged deal information, not just a number pattern. 
#2 – Personal Email Account Use
The “From” address is “raul.ramirez@gmail.com,” a personal account, not a corporate one. This is a policy violation alert not caught by the traditional DLP solution. 
#3 – Market-Moving, Material Nonpublic Information (MNPI)
The red highlight of the phrase “acquisition of the only oil refinery in the San Francisco Bay Area,” triggers an alert for potential SEC violation as it discusses market-moving information without an NDA in place, a context traditional DLP can’t grasp but AI easily understands from its entity awareness of the business’ logic and business logic.
#4 - Privacy and Regulation: PII & CCPA Violation
In this example, highlighted in orange, sharing a recipient’s personal address “3 Auburn Street, San Jose, CA” externally is a direct violation of a California Consumer Privacy Act (CCPA) risk.
#5 – Data / Record Integrity: Inaccuracies and Hallucinations
In addition to the violation in #4, the “home address,” highlighted in orange, does not match organization records, meaning that this is likely a hallucination or data error, which can lead to compliance and legal risks.
Additionally, highlighted in pink, the mention of “best wishes to your wife, Clara,” when corporate records indicate the spouse’s name is different (Alice), is a subtle but critical sign of record integrity risk.
Why Traditional DLP Isn’t Enough
In the above example, the existing traditional DLP solution was only able to potentially identify two items that could be of risk. One was a reference number, which had no value and the other was a savings account number, which could be of potential risk. The risk for each of these, is however, exacerbated by the other risky elements (that went undetected) contained in the email, such as sending it to the wrong recipient, sending it via a personal email account, and disclosing NDA-protected information.
Traditional DLP provides only surface-level protection, as it relies on explicit instructions and rigid rules to catch threats. This approach means it can only identify risks that have been predefined, such as certain keywords or patterns, and it often misses important contextual cues, the intent behind communications, or subtler forms of data leakage.
Because of its inflexible pattern detection, traditional DLP can generate a high number of false positives. These unnecessary alerts disrupt workflow, block legitimate communications, and ultimately frustrate employees, impacting productivity and business operations.
Worse yet, traditional DLP often has significant blind spots. It struggles to detect complex risks like data disclosed in context, discrepancies in record integrity, violations of internal policy, or breaches of regulatory requirements. These kinds of threats can easily fly under the radar, leaving organizations exposed to advanced and evolving risks.
How Bonfy’s Next-Gen DLP Closes the Gaps
In contrast, Bonfy’s next-gen DLP closes these gaps by incorporating contextual awareness, allowing it to understand not just the content of communications, but also the relationships among parties, corporate policies, and the accuracy of existing records. Its deep content analysis capabilities enable the identification of subtle threats, such as unauthorized financial disclosure, mismatched recipients, or even fabricated details within a message.
Finally, Bonfy’s adaptive risk intelligence tailors its protection to the unique characteristics and needs of each organization, reducing false positives while uncovering advanced and subtle risks that legacy systems would typically overlook.
TL: DR
Legacy DLP solutions are outpaced by today’s dynamic data environments. As organizations embrace digital collaboration, the danger of undetected, and unintentional, data leaks only grows. Bonfy’s AI-driven next-gen DLP ensures you don’t just check a compliance box, but proactively protect your business, your data, and your reputation from evolving risks.
