It’s clear that organizations need more robust capabilities for detecting sensitive data, monitoring access and usage, and tackling misconfigurations and vulnerabilities.
Multiple market forces have brought us to this moment. The rise of AI, machine learning and surging interest in generative AI have increased the volume of data that organizations store in the cloud, creating new risk vectors related to the models themselves. Cloud service sprawl and multicloud deployments add complexity and create fluid environments where sensitive data is hard to monitor. Regulatory compliance has become a major concern, with data privacy and protection regulations creating the possibility of severe consequences for companies that fail to find, classify, and reasonably protect regulated data sets.
The market has spoken: AI, cloud sprawl, and data regulation drive demand for a holistic, platform approach.
In tackling these growing challenges, organizations have come to realize that fragmented, legacy point solutions cannot be the way forward. Organizations are demanding better controls around data to support their increased appetite for innovation — but in 2023, there is neither budget nor spare working hands to manage dozens of different security tools. Data security has to become a holistic and native aspect of the tools that organizations are already using.
CNAPPs are integrated security solutions that address the entire development lifecycle and runtime operations for cloud-native applications. They consolidate infrastructure-as-code (IaC) scanning, cloud security posture management (CSPM), workload protection (CWPP), software composition analysis (SCA), and other capabilities, with the goal of identifying and prioritizing risk across cloud applications and infrastructure.
Where does data security come in? Adding data context allows security teams to zero in on the most important vulnerabilities and misconfigurations, while providing more actionable paths to remediation when data risk is detected.
Cloud-native development results in a sprawling attack surface spanning containers, virtual machines (VMs), serverless platform-as-a-service (PaaS), IaC, and more. When there are so many components to secure, there’s a lot that can go wrong. Accordingly, CNAPPs encompass many different monitoring capabilities across application development, staging, and runtime.
Prioritization is a major challenge. It’s nearly impossible for security teams and developers to address every flashing light on their dashboards. They need to decide which incidents require immediate remediation, which can be deferred, and which can be automated. Attack path analysis is used to help determine which vulnerabilities can lead to significant breach of sensitive data or a major compliance violation, by looking at:
To understand risk, we have to understand data. Data is both the lifeblood of the modern organization and the prime target for attackers; it is also at the heart of compliance frameworks such as PCI DSS.
Discovering, classifying, and monitoring sensitive data are key to effective attack path analysis. E.g., an unpatched VM might not be a very serious problem, from a security perspective; but if it’s running a database that stores customer information and is now exposed to the world, it needs to be addressed immediately. Delineating between these scenarios cannot be done solely on the configuration level and requires insight into data content and context.
We can think of the Log4Shell vulnerability, which impacted countless applications: in some enterprises, there were potentially hundreds of codebases that relied on the Log4J framework and were now at risk. Security teams had to decide which of these to patch first, an often-painful process which would include upgrading dependencies and potentially lead to application downtime. Knowing where critical data was at risk could help security teams prioritize remediation, and save a tremendous amount of time and resources.
Data-centric security and cloud-native application protection are the ultimate ‘better together’ — feeding into the other’s strengths to deliver a more complete and streamlined solution for security, data, and development teams. This combination promises many benefits for customers using Dig Security, as well as those who rely on Palo Alto Networks’ Prisma Cloud.
By combining Prisma Cloud's CNAPP with Dig's data-centric approach, users will be able to gain end-to-end visibility and control over their sensitive data across the software development lifecycle (SDLC). There’s a lot of potential here to build on Dig’s data-centric approach and enhance it, in areas such as:
For Prisma Cloud customers, Dig will bring additional capabilities for understanding and securing sensitive data within cloud environments — allowing for robust CNAPP protections to be applied alongside granular data security and compliance in complex cloud ecosystems. For example:
Forward-Looking Statements
This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, but not limited to, statements regarding the anticipated benefits and impact of the acquisition. There are a significant number of factors that could cause actual results to differ materially from statements made in this blog, including, but not limited to, the ability of Palo Alto Networks to integrate Dig’s technology, operations and business. Additional risks and uncertainties that could affect our results are included under the captions "Risk Factors" and "Management's Discussion and Analysis of Financial Condition and Results of Operations" in Palo Alto Networks’ Quarterly Report on Form 10-Q filed with the SEC on November 17, 2023, which is available at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov. Additional information will also be set forth in other filings that Palo Alto Networks makes with the SEC from time to time. All forward-looking statements in this blog are based on information available as of the date hereof, and we do not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.
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