Cloud DLP (Data Leak Prevention): The Essential Guide
The Nightfall Team
October 17, 2023
Cloud DLP (Data Leak Prevention): The Essential GuideCloud DLP (Data Leak Prevention): The Essential Guide
The Nightfall Team
October 17, 2023

Cloud DLP (Data Leak Prevention): The Essential Guide

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Updated: 5/21/24

Cloud DLP, or cloud data leak prevention (DLP), is a critical aspect of data security that refers to the process of preventing sensitive data from being leaked or lost in cloud environments. Cloud DLP products, such as Nightfall AI, introspect data within software-as-a-service (SaaS) applications via their APIs. In this guide, we will explore the importance of cloud DLP, as well as what practitioners should look for when it comes to selecting a cloud DLP tool. 

What are the risks of working in the cloud?

The cloud has become increasingly relevant as more and more businesses migrate to tools like Slack, Google Drive, Jira, and GitHub. Furthermore, Deloitte predicted that 70% of companies that adopt AI will also do so via the cloud. In short, it would be a colossal understatement to say that the cloud has changed the way we work—however, as cloud technologies evolve, so do security threats. Here’s a brief overview of data risks in the cloud:

  • Data Sprawl: Data sprawl refers to the rapid increase and dispersion of data across various locations, applications, and services. As organizations generate and store vast amounts of data, it becomes increasingly difficult to keep track of what data they have, where it is located, and who has access to it. This can lead to increased security risks and challenges in data governance and compliance.
  • Data Exposure: Data exposure occurs when sensitive or confidential information becomes accessible to unauthorized individuals. This can happen due to inadequate protection of an application, misconfigurations, inappropriate usage of data systems, and overly permissive access controls. Data exposure often indicates missing security controls or processes.
  • Data Leaks: Data leakage refers to the unauthorized transmission of data from within an organization to an external destination or recipient. If data sprawl refers to where data is shared, data leakage refers to who that data is shared with. For instance, if an insider exfiltrates data, or if data otherwise falls into the hands of a third party (e.g. an LLM like OpenAI), then that data is considered “leaked.” Data leakage can be intentional or accidental. Data leakage can also lead to serious consequences, including financial loss, reputational damage, and legal issues.
  • Data Misuse: Data misuse can occur when data is used in a way that is not in line with its intended purpose or the terms of its collection. Data misuse can lead to privacy violations, breaches of trust, and potential legal consequences.
  • Data Loss: Data loss refers to the unwanted removal of sensitive information either due to an information system error or theft by cybercriminals. Data loss can occur due to various reasons such as accidental deletion, hardware failure, or cyber attacks. Data loss can lead to financial loss, reputational harm, and compliance risks.

What are the different types of DLP?

While traditional DLP methods worked in a pre-cloud world, they’re not able to mitigate the risks outlined above. Let’s take a look at the variations of DLP methods and how they stack up:

  • Log analysis is the practice of evaluating event logs to determine who had access to what data, and when. While this is a useful tool for discovering data leaks, it doesn’t help with remediating sensitive data sprawl or leakage.
  • Network proxies, or network DLP, monitor and protect data in transit over a business’ network. Network proxy services also have a significant gap in coverage, as they can only monitor encrypted data in motion—thereby ignoring data that’s at rest. These services are very latency-sensitive, meaning that only simple, low-latency detection algorithms can be used; this leads to lower accuracy overall. Accuracy is also impacted by network proxy services’ lack of contextual understanding on how the data in question is being used or shared. By misinterpreting this context, the proxy can make incorrect decisions and rely on blunt or immature policies.
  • Forward proxies use agents to monitor all traffic to business’ endpoint devices—including traffic involving shadow IT. However, in today’s “Bring Your Own Device” (BYOD) work environment, forward proxies can be both difficult to enforce as well as time consuming to roll out to all devices. When misconfigured, forward proxies can also negatively impact workflows by causing lags and otherwise failing to protect users. 
  • Reverse proxies intercept user requests to business’ servers in real time. While useful for protecting servers, reverse proxies come with several limitations. For instance, like forward proxies, they require a lot of time and effort to deploy, and can cause lags and other performance failures that interfere with employee performance. Unlike forward proxies, though, they can’t offer any insights into shadow IT.
  • Endpoint DLP agents monitor servers, computers, laptops, and mobile devices on which data is used, moved, or saved. The application scans the file system of the device as well as the input/output of the device. Endpoint agents can only be installed on managed devices, so they don’t provide visibility into environments where employees are connecting to SaaS applications via their own devices. Similarly, agents have CPU overhead and can introduce latency.
  • API introspection is a technique used by cloud DLP solutions to identify sensitive data in the cloud. This technique involves analyzing the data in use and at rest in SaaS applications via their APIs. SaaS applications have APIs that allow users to programmatically access data and take application-specific actions. For example, you can use the Slack API to read Slack channels and delete messages. API introspection allows cloud DLP products to connect to SaaS applications and to identify sensitive data in near real-time and take appropriate action to prevent data loss. API introspection gives cloud DLP services granular context through metadata. API introspection is out of band so it does not introduce any network latency and is easy to deploy in a few clicks without any agents. API introspection also allows for granular control over data, which can help to ensure that sensitive data is properly protected.

As you may have guessed, Nightfall falls into this last category: API introspection. Let’s dive a little deeper into what this can help businesses to accomplish. 

What can API introspection-based cloud DLP achieve?

Whether you’re looking to discover your data or meet compliance standards, cloud DLP can help businesses to achieve several critical goals. 

Identify the locations of sensitive data storage and transmission.

  • Question to ask: Are there mechanisms currently in place to identify or monitor sensitive data within existing tools?
  • Solution: Implementing a cloud DLP solution can help locate sensitive information that is either stored in specific areas within your organization’s infrastructure or being sent without secure measures via SaaS communication platforms.

Pinpoint deficiencies in staff security training.

  • Questions to ask: How often is data security training occurring? How specific is the content of these trainings, and what are the retention and satisfaction rates among employees?
  • Solution: Automated features in cloud DLP tools enable real-time, situation-specific training on handling sensitive information whenever a violation is detected. Given that this training is directly related to the employees' current actions, it is far more likely to influence and improve their future behavior. Likewise, remediation capabilities empower employees to self-heal data exposures risks on their own, lessening the burden on the security team, and accelerating the time to resolution.

Mitigate data exposure and leakage risks.

  • Questions to ask: Is sensitive information currently stored and transferred in an insecure manner, thereby exposing the company to the risk of data breaches? Is sensitive information accessible at rest within SaaS apps that have months or years worth of data retention?
  • Solution: DLP solutions identify sensitive data elements and highlight business processes that lead to insecure storage and transmission. This will allow you to modify those identified procedures to enhance the secure handling of sensitive information, consequently lowering the organization's overall risk.

Enhance and maintain regulatory compliance.

  • Questions to ask: Does the lack of a robust system for tracking sensitive data compromise our ability to meet various regulatory requirements, leaving us vulnerable to penalties and sanctions?
  • Solution: Implementing a cloud DLP solution not only helps in locating and securing sensitive information but also aids in meeting compliance standards. By monitoring data in real-time and generating reports, DLP tools provide the necessary documentation to prove compliance with regulations such as GDPR, HIPAA, and PCI-DSS. This helps to mitigate legal risks and ensures that the organization is operating within the confines of the law.

What makes cloud DLP unique?

Cloud DLP is a strategic approach to stopping sensitive data sharing outside of authorized apps. Unlike network DLP and endpoint DLP methods, cloud DLP can connect with GenAI tools and SaaS apps via seamless API integrations. This enables a number of important benefits.

  • Cloud DLP is not confined to a single network or endpoint. That’s important because your employees aren’t either; today’s workforce is increasingly distributed, spanning disparate devices and networks.
  • Cloud DLP can inspect files of varying types and sizes to ensure complete coverage. Proxies optimize for low latency, which means dropping big files or folders that can slow down network operations. On the other hand, cloud DLP has no impact on the network, so files of varying size and file type are not an issue.
  • Cloud DLP does not require a proxy or agent. Proxies are a single point of failure, not to mention a major employee privacy concern. TLS is terminated at the proxy, and all network traffic gets scanned (including employees accessing personal information like their bank accounts).
  • Cloud DLP is superior for employee privacy. Cloud DLP respects end-user privacy because it does not intercept network traffic via a proxy or monitor endpoint activity via an agent.
  • Cloud DLP can monitor and remediate sensitive data both historically and in real time. Data at rest is a major risk vector because there may be sensitive data already embedded in SaaS apps, lurking in the shadows.
  • Cloud DLP remediates at a granular, app-specific level. Cloud DLP can remediate singular instances of sensitive data, which means that employees can get the full value out of tools like ChatGPT without being blocked. For example, you might change the sharing settings of a file in Google Drive, or redact a snippet of text in a Slack message. These are much more specific alternatives to blocking network traffic, which is frustrating to end users, and will likely lead to shadow IT.
  • Cloud DLP unlocks broader coverage of your stack. If installed as a browser extension, cloud DLP can protect employees even if they use shadow IT to access unsanctioned apps. Similarly, cloud DLP is API powered, so you can use APIs to inspect data anywhere—including on infrastructure the DLP service can’t access.

Based on these benefits alone, it’s clear that cloud DLP is a powerful tool that can stop data sprawl and leakage in its tracks. However, it doesn’t stop there: These days, cloud DLP solutions like Nightfall are able to offer advanced features via AI. AI-native cloud DLP tools present an additional set of benefits, such as:

  • Better time to value: Some cloud DLP solutions, like Nightfall, offer pre-trained detectors that are ready right out of the box. This means that installation takes only a few minutes, as opposed to several months.
  • 24/7 monitoring: AI tools can keep watch via an “always on” detection engine.
  • Industry-leading accuracy: AI-powered detection is significantly more accurate than regex and rules-based detection because it uses neural network embeddings to consider the context surrounding a violation. Detectors can also be trained over time to escalate only the highest priority security risks.
  • Quicker incident response: According to IBM’s recent data breach report, AI helps security teams to respond to data breaches over 30% faster—which saves businesses an average of $1.8 million in the process. This is, in part, due to AI’s ability to deploy automated alerts.
  • Long-term employee education: Since AI can detect security violations in real time, it can also respond in real time by both alerting the security team of the violation, as well as notifying the employee who made the violation. This provides a highly contextual learning experience, which employees are more likely to recall and implement in the future. Over time, this can be an incredibly useful tool for developing a strong culture of security.

Now that you’ve got the full picture, you can see the multitude of ways that cloud DLP ensures security and compliance, while also chipping away at security teams’ workloads over time.

What are the limitations of cloud DLP?

Cloud DLP is a critical component to any holistic security strategy. However, like any other kind of DLP, it’s not perfect. Here are a few limitations you might encounter while using cloud DLP:

  • Cloud DLP is a new and developing field. That means there aren’t a ton of modern, enterprise-grade tools to choose from. 
  • Cloud DLP doesn’t inspect or block network traffic. If you’re looking to inspect and block network traffic inline, you’ll need a proxy.

In summary: it’s important to have a firm grasp on both the benefits and limitations of cloud DLP before making a selection among existing tools.

How can you decide which cloud DLP solution is right for you?

When you’re on the lookout for a cloud DLP solution, it’s important to keep your business’ unique needs in mind. Here are a few key questions to consider along your DLP journey:


  • What apps are you looking to cover? Does your proposed DLP solution cover the majority of these apps, or would you need a patchwork solution for complete coverage?
  • What kind of sensitive data poses the most risk to your business? Does your DLP provider have specialized detectors for this data type?
  • What is the installation process like? How quickly do you anticipate seeing value after installation?
  • Does your DLP provider offer both near real-time and data at rest scanning?


  • How accurate is your DLP provider’s detection? Do they use AI to enhance their detection?
  • What practices does your DLP provider have in place to cut down on false positive alerts?
  • How large is your Data Science team and what expertise does your company have in machine learning model development, natural language processing and deep learning, model deployment, and model scalability?


  • Does your DLP provider send context-rich alerts? And can those alerts be sent to your SIEM or communication app of choice?
  • What remediation options does your DLP provider have? Can remediation be automated or sent to the end user?


  • Does your DLP provider have a smooth user experience, along with robust help documentation?
  • Does your DLP provider offer a dedicated team to provide ongoing support, like detector tuning?


  • How effective will the DLP provider be as a long-term strategic partner? What are the backgrounds of their teams, their investors, and fellow customers?
  • How does your DLP provider cultivate a stronger culture of security?

These questions are a starting point to help you to determine your security and compliance needs, as well as the cloud DLP solution that is best suited to meet those needs.

What types of information does cloud DLP protect?

In order to determine if a cloud DLP solution is right for you, you have to consider the kinds of sensitive information that cloud DLP can protect. 

  • Personally Identifiable Information (PII): Includes data like names, addresses, phone numbers, and social security numbers. If PII is exposed, it could lead to identity theft, financial loss, or other forms of personal harm. Unauthorized individuals could use PII to impersonate someone, open fraudulent accounts, or even conduct social engineering attacks.
  • Payment Card Information (PCI): Includes credit card numbers and card metadata. Leakage of PCI can result in financial theft. Fraudsters could make unauthorized transactions, affecting both individuals and businesses monetarily.
  • Protected Health Information (PHI): Covers medical records, insurance information, and other health-related data. Exposure of PHI can lead to medical identity theft, insurance fraud, and disclosure of highly sensitive health conditions. Such exposure could also result in stigmatization or discrimination based on medical history.
  • Secrets and credentials: Refers to API keys, passwords, and other authentication mechanisms used to secure applications and systems. If these secrets are leaked, unauthorized users can gain access to secure systems, potentially compromising the data stored there and even carrying out malicious actions under the identity of the authenticated user or system.
  • Intellectual property (IP): Encompasses proprietary algorithms, source code, patents, business strategies, and more. Leaking intellectual property can give competitors or malicious entities a strategic advantage. It could result in significant financial loss and reduce a company’s competitive edge.

What is Nightfall AI?

Nightfall is the AI-native DLP platform for your SaaS and GenAI stack. Organizations use Nightfall to reduce the risks of data exposure, ensure compliance with leading security and privacy standards, and empower employees to work safely with modern SaaS and GenAI apps. Nightfall AI was founded in 2018 and hundreds of innovative companies use the platform to protect sensitive data.

What does Nightfall do? 

Nightfall automatically detects over 100 sensitive data types like PII, PHI, PCI, secrets, and credentials in order to achieve and maintain compliance with leading industry frameworks like ISO-27001, SOC 2, HIPAA, and more. We offer comprehensive cloud coverage via four core products:

  • Nightfall for Sensitive Data Protection integrates directly with SaaS apps like Slack, GitHub, Jira, and more to monitor for sensitive data in near real-time. When Nightfall detects sensitive data, it sends an alert to Nightfall’s intuitive user console or communication app of choice. From there, security teams can see context-rich insights about violations, deploy remediation actions, and send notifications to employees without having to go to another app.
  • Nightfall for Data at Rest scans all data historically in connected SaaS apps. There are vast amounts of data at rest that are retained by SaaS apps. This data may not be in active use, but it’s lying in plain sight for those who go looking for it. Nightfall traverses this historical data and pinpoints sensitive information to provide you with actionable insights.
  • Nightfall for ChatGPT is a browser extension that intercepts sensitive data before it’s transmitted to OpenAI. It also automates end-user remediation to educate employees about security policies in real time—while simultaneously reducing security team workloads.
  • Nightfall’s Firewall for AI gives technical teams access to the AI-powered Nightfall detection engine as a set of APIs. You can use these APIs to inspect, classify, and protect data anywhere, as well as to embed DLP capabilities into your own business logic.

Why AI-Native DLP?

There a handful of possible data detection methods in cloud DLP, including:

  • Machine learning / artificial intelligence: AI models can be trained to identify sensitive data based on its content and surrounding context. Models can handle unstructured and structured data alike, pinpoint data elements with high accuracy due to training, and improve and adapt over time based on feedback.
  • Regular expressions (regexes): A regex is a sequence of characters that defines a search pattern in a given text or string. Regexes can be used to identify sensitive data based on patterns in the data. Regex-based detection is limited to identifying specific patterns in data, which means that it may not be effective in identifying more complex data elements.
  • Data fingerprinting: Data fingerprinting involves creating a unique signature for each piece of sensitive data. This signature can then be used to identify the data in the future, even if it has been modified or moved to a different location. Data fingerprinting is limited to identifying known data, which means that it may not be effective in identifying new or unknown types of sensitive data.
  • Public cloud APIs: Less mature cloud DLP solutions may be unable to build an in-house data science team, and, as a result, likely leverage APIs from Google, Amazon, or Microsoft. Each public cloud has their own data classification services, along with hundreds of other offerings. The detectors that these services offer tend to be low accuracy and based on simple rules or heuristics, with a focus on quantity over quality. Moreover, they often produce unexpected results. And when things go wrong, there’s no one who can help with model scalability, accuracy, and fine tuning.

At Nightfall AI, our in-house data scientists build, maintain, monitor, and continuously improve our AI-powered DLP capabilities. This gives us significant advantages over DLP solutions that rely on public cloud APIs. Just to name a few, Nightfall’s AI-powered DLP has:

  • Superior performance: We benchmark our DLP against public cloud APIs weekly. Our precision, recall, and speed consistently outperform these services overall.
  • Enhanced data privacy and security: Using third-party APIs can add data risk surfaces, as they may pass your data onto a third party vendor. Instead, at Nightfall, you have control over your data.
  • Customized detection: Using third-party APIs means the vendor has a one-size-fits-all solution with no control over model tuning. At Nightfall, our data scientists work directly with you to optimize detection for your unique data and use cases.
  • Ongoing enhancements: We continuously monitor detection performance across customers and integrations. This allows us to rapidly identify and resolve any detection gaps.

The Nightfall difference is simple: Our in-house DLP expertise delivers better detection and protection for your sensitive data.

How does Nightfall work?

Nightfall is the first AI-native DLP platform because AI is the core of the Nightfall detection engine. Nightfall’s AI-powered detectors use neural network embeddings to identify PII, PCI, and PHI as well as secrets and credentials. AI enables the Nightfall AI detection engine to deliver accurate, context-aware findings. 

Context Awareness

Imagine a scenario where an engineer inadvertently enters a Social Security Number (SSN) into a ChatGPT prompt. Nightfall doesn't just employ rudimentary pattern matching; it leverages advanced context-aware algorithms to scrutinize the content. At the most basic level, any numerical sequence conforming to the SSN pattern will be tagged with a "Possible" confidence level, serving as a baseline alert.

However, Nightfall's real prowess manifests when contextual data is available. For instance, if the SSN is surrounded by contextual cues such as the phrase "Please verify this SSN," the system's confidence level escalates to "Likely" or "Very Likely." This nuanced understanding of context drastically reduces the rate of false positives, thereby optimizing the incident response pipeline for security teams.

This is more than simple text analysis; it's about leveraging machine learning to understand the semantic landscape in which data resides, delivering a highly refined and precise DLP mechanism.

Adaptive Intelligence

Nightfall's detection engine isn't a static set of algorithms; it's an evolving, learning system fine-tuned by the very people who use it. The engine has two major avenues for customization: User feedback and rule extensibility.

Take the SSN detection example again. If a security alert goes off in the Nightfall console, your security team isn't just a passive recipient. They can actively label the alert as a "True Positive" or categorize it under specific types of false positives like "Not an SSN" or "Not a violation." This user-annotated data is integrated back into the machine learning model, which is constantly refining its own decision-making processes. The more granular feedback your team provides, the sharper and more responsive the detection algorithms become, thereby reducing false positives and enhancing overall system accuracy.

But what if your security posture demands immediate, custom solutions? Nightfall has you covered. It allows users to extend existing detectors by defining bespoke detection rules tailored to the unique security requirements and risk thresholds of your organization. This isn't merely a way to augment detection capabilities; it's a powerful method to alleviate “alert fatigue” by minimizing irrelevant or low-priority notifications.

In essence, Nightfall offers an ever-evolving, two-way interactive security mechanism. It's not just a tool but a collaborative platform that adapts to the unique aspects of your security ecosystem.

How do you implement Nightfall?

Nightfall installs via APIs that connect seamlessly with popular SaaS apps like Slack, Google Drive, Jira, GitHub, ChatGPT, and more. For each individual app, the implementation process looks something like this:

  1. Log in to the Nightfall dashboard.
  2. Connect with your SaaS app or GenAI tool of choice via OAuth integration.
  3. Implement baseline detection and policy templates for relevant classes of sensitive data.
  4. Review accuracy and fine-tune policies to ensure high signal to noise ratio.
  5. Review internal policies around the types of data you expect to see in the SaaS app and what controls or limitations you want to put in place.
  6. Enable automatic notifications to end users when they share or expose sensitive data so that they can self-heal these issues. Include educational references or links to policies.
  7. Review violations and take action on them, whether manually or automatically with workflows.
  8. Review violation trends and benchmarks over time to evaluate the security posture at the organization and identify opportunities for employee education.

What’s next?

Schedule time with one of Nightfall’s product specialists here, or email us at at to set up a personalized demo of our cloud DLP platform.

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