Securing Data in the Age of Digital Transformation

Introduction

Digital transformation is changing the way organizations operate and deliver value to customers. However, it also brings new data security challenges that must be addressed. In this age of digital transformation, data has become the most valuable asset for many companies. Therefore, it is crucial to put proper data security measures in place.

As companies adopt new technologies like cloud computing, IoT devices, and AI, they need to reassess their data security strategies. New threat vectors emerge while increased connectivity expands the potential attack surface. Meanwhile, data is now created, processed and stored in more disparate environments. This complexity makes it harder to maintain control and visibility.

In the following sections, I will explore the new data security challenges arising from digital transformation trends. I will also provide recommendations on how to mitigate risks and protect sensitive data in this new landscape. Bolstering data security is essential to fully realize the benefits of digital transformation without jeopardizing critical assets.

New Threat Vectors Created by Digital Transformation

Digital transformation brings many new technologies into the enterprise environment. Each of these technologies has the potential to introduce new data security risks if not managed properly.

Cloud Computing

Cloud computing offers advantages like scalability, flexibility and cost savings. However, storing data with cloud providers also relinquishes some control. Cloud security is a shared responsibility between the provider and customer. Organizations need to ensure proper safeguards given this split responsibility model.

Some of the top cloud security risks include:

• Data breaches due to misconfigurations or insider threats at the cloud provider
• Insufficient identity and access controls leading to unauthorized access
• Lack of visibility into cloud data, workloads and user activities
• Inconsistent security policies across cloud and on-premises environments
• Failure to encrypt sensitive cloud data both at rest and in transit

Internet of Things (IoT)

The growth of IoT devices in the enterprise poses new data security concerns. IoT sensors and endpoints are often deployed in insecure environments, yet collect critical data and can provide access to corporate networks.

Some of the top IoT-related security risks are:

• Unsecured endpoints that are easy for hackers to penetrate
• Weak default credentials that are never changed after deployment
• Lack of device-level encryption exposing data at rest and in transit
• Inadequate network segmentation that allows lateral movement after breach
• Minimal device security patching due to lack of oversight
• Privacy concerns with extensive data collection in public or private spaces

Artificial Intelligence (AI)

AI systems are data-driven and thus require strict data security controls. The data used to train AI algorithms may be sensitive and valuable. In addition, attackers are looking to poison training data sets to manipulate AI behavior.

Some of the unique AI security risks include:

• Data poisoning where bad data intentionally alters models
• Model theft through unauthorized access or theft of intellectual property
• Adversarial attacks specially crafted to cause misclassifications
• Biased outcomes due to issues with training data integrity
• Inconsistent model performance when operating under different conditions

Evolving Data Landscapes

Digital transformation has led to expanded data ecosystems that are more distributed, dynamic and opaque. This environment makes it much harder to maintain total visibility and control:

• Proliferation of endpoints: Data is now collected and processed by cloud services, IoT devices, edge computing resources, mobile devices and more. There are more endpoints to secure across fragmented environments.

• Dynamic data pipelines: Real-time data integration has become crucial. As machine learning models iterate, new data feeds are continuously added to refine outputs. The sources and flows of data are constantly changing.

• Obscured visibility: With data stored across hybrid cloud environments, including multiple public clouds, visibility is obscured. Comprehensive data mapping, classification and monitoring is difficult.

• Decentralized governance: Shared responsibility models, third-party cloud providers and on-demand infrastructure make unified governance a challenge. Policies and controls cannot be standardized across all data platforms.

These trends expand the potential attack surface while weakening centralized control and visibility. To bolster security, data management and protection must become contextual and adaptive.

Strategies for Securing Data in the Digital Age

Based on these new challenges, organizations should take a proactive approach to overhaul data security strategies. Here are some best practices for securing data in the digital age:

Adopt a Zero Trust Model

The zero trust model assumes that all network traffic and access requests are potentially untrustworthy. This model requires continuously validating connections and entitlements before granting the least privileged access. For securing data, zero trust principles dictate that sensitive data should be encrypted, privileged user access tightly restricted, and multi-factor authentication required for data access.

Improve Visibility into All Data

Gaining better data visibility is critical for managing security risks. Organizations should maintain inventories of business data and map data flows across hybrid environments. Data discovery tools, fueled by machine learning, can identify unknown sensitive data. Data loss prevention (DLP) solutions can monitor and alert on unauthorized data exfiltration.

Implement Granular Access Controls

As data platforms diversify, access controls should be granular and identity-centric. Use role-based access control (RBAC), attribute-based access control (ABAC) and user entitlement reviews to restrict access to only authorized individuals. Deploy user and entity behavior analytics (UEBA) to detect suspicious access patterns.

Embrace Data-Centric Security

Legacy perimeter defenses are insufficient for modern data platforms. Data-centric security safeguards data itself via dynamic controls that follow data everywhere. Data encryption ensures data is unusable without keys. Tokenization substitutes sensitive data fields with tokens to avoid exposing raw data.

Architect Security into Data Pipelines

Shift left on security by embedding controls directly into data pipeline architectures. Perform static application security testing (SAST) and dynamic application security testing (DAST) on pipelines. Deploy data loss prevention (DLP) capabilities at data ingest and along the pipeline. Test data outputs for signs of poisoning or manipulation.

Plan for a Multi-Cloud Environment

Accept that most organizations will utilize multiple cloud providers. Plan for multi-cloud by Normalize security policies across cloud and on-premises environments. Also implement cloud access security brokers (CASBs) to centralize visibility, access controls, threat detection, encryption and other security functions across cloud services.

Conclusion

To fully leverage digital transformation, companies must also transform data security. With critical business data flowing across hybrid environments, security leaders face new challenges related to visibility, governance, access control and threat prevention. By embracing emerging data-centric security principles and technologies, organizations can strike the right balance between digital innovation and robust data protection.