As organizações modernas centradas em dados exigem uma nova Abordagem baseada em resultados para DLP

Implementando Prevenção contra perda de dados (DLP) technology has historically been a struggle for organizations and security teams as there is a constant battle of tuning just right to stop sensitive data from needlessly leaving the organization while not impeding the course of business at the same time.

Modern data usage requirements are leading organizations to re-evaluate utilizing traditional DLP approaches and instead focus on outcome-based results.

While legacy DLP monitors data in transit across the network, newer approaches — such as DLP na nuvem -— starts at the source and secures data stored and shared in cloud environments. Cloud DLP, is by nature outcome-based as it focuses beyond technology controls to also encompass processes and practices.

This approach more efficiently detects and prevents violations to corporate policies regarding the use, storage, and transmission of sensitive data; enabling organizations to enforce policies to prevent unwanted dissemination of informações sensíveis — without hampering the flow of business.

DLP technology controls are more effective in protecting key organizational data when incorporated with other data management and segurança de dados measures including: descoberta, classificação, controles de acesso, remediação, etc. Once sensitive data has been identified, secured and governed this is a basis for an effective data security backstop including the following use cases:

• Risco de insider
• IA generativa
• Confiança zero

Legacy network-based DLP is often too little too late when used as a primary control for insider risk

Unfortunately, too often effectively mitigating insider risk and DLP have been used as synonymous terms. That is comparing “apples to oranges”. DLP is simply a final control attempting to prevent sensitive data from leaving the organization. Whereas managing insider risk is a much broader discipline that includes:

• Identifying data and individuals impacted
• Defining and controlling your data
• Creating and applying policies
• Creating rules of engagement
• Building a risk team
• Refining consistent processes
• Training users
• Monitoring data use an user activity

The legacy DLP approach attempts to prevent data that meets poorly defined criteria from leaving the organization. Whereas cloud DLP is far more effective in managing insider risk as it is more preventative and starts with identifying, classifying, securing and governing sensitive data before users even get a chance to access, move or send it.

AI-enabled data discovery & classification, identifying improper access and applying remediation can reduce access to sensitive data by well over 90% when done properly. This takes a lot of pressure off of the security team when trying to apply other policies and security controls and DLP. This also increases engagement from the business team and users as the managing insider risk process seems more manageable.

Generative AI requires new data-centric controls to ensure data security and governance

Generative AI is a generational transformational technology. Just like the personal computer and smartphones it isn’t a matter of “if” but a matter of “when” for adoption. As such, organizations are scrambling to secure sensitive data that might flow through GenAI training models. The main debate is where during the gen AI lifecycle is the best place to apply security controls and ensure that the organization protects its key data assets. The possible points are:

• Inclusion of sensitive data controls in the creation of the algorithm for the model
• Selection of APIs
• Consumption of data into the model
• Management of LLM
• Creation of output through user interface

While all steps are logical options, all have limitations. Trying to include sensitive data management controls in the algorithm, or even through the selection of APIs, leaves too much to chance and can also reduce the effectiveness of the model. Management of the LLM is difficult as the LLM is often stored in a vector database as mathematical equations without any direct data visibility. Finally, users have too many options at the output stage including a wide variety of ways to access the model through different interfaces and browsers.

What’s left is applying governance and security at the point of source data that is feeding the model. Network-based DLP won’t help, but a cloud DLP outcome-based approach starting at the data element can. Deep and broad discovery and classification, especially for unstructured data sets, can identify key organizational data and controls can be applied to prevent crown jewel data, privacy data, and other sensitive data from being used to populate the LLMs. Applying proactive controls on data before it ever starts the generative API controls greatly mitigates risk and makes further controls down through the process including DLP is much more manageable.

DLP ≠ Zero Trust

Legacy DLP approaches can’t come close to guaranteeing Confiança zero. Instead, Zero Trust requires a variety of data management capabilities combined with data security controls.

You can’t govern and control what you don’t know. AI-enabled data discovery not only surfaces the “what”, but also the context and relationships of data. Sensitive data is identified through the discovery process and in-depth classification ensures that data and metadata is reliably tagged.

Once initial and ongoing discover and classification is established, data security controls can be effectively applied including:

• Governança de acesso a dados
• Remediação automatizada
• Rights Management
• Minimização de dados
• Secure browsers
• DLP
• Obfuscation (encryption, masking, redaction, tokenization)

Data access governance also starts at the discovery process. As data is scanned, overexposed and over-privileged is identified and remediation of access violations needs to be automated as much as possible. By removing improper access and identifying and deleting dados obsoletos the workload for DLP and obfuscation technologies is greatly reduced.

Incorporating a data-centric cloud DLP approach into a Zero Trust strategy enhances data security controls and ensures a comprehensive approach to safeguarding sensitive information.

Building an outcome-based DLP approach involves that it is just one of the data management and security controls applied to scenarios such as Insider Risk, Generative AI, and Zero Trust. Tailoring DLP to an outcome-based approach ensures a robust and adaptive data protection framework in today’s dynamic cybersecurity landscape.

To learn more about how BigID enables data security, including DLP— Agende uma demonstração individual com nossos especialistas hoje mesmo.

Autor

Alan Dayley

Diretor de Relações com Analistas

Depois de 14 anos gratificantes como analista de pesquisa na Gartner, especializado em gerenciamento de dados, Alan deu o salto da rotina de analista para o mundo dinâmico das relações com analistas na BigID. Sua jornada foi definida por uma paixão por entender a importância dos dados na vida das pessoas, decorrente de minha experiência diversificada em gerenciamento de produtos, marketing e inteligência competitiva nos campos de armazenamento, gerenciamento de desktops e software de rede.