Data Visibility and Protection: The Foundation of Modern Cybersecurity

Explore challenges and solutions in data visibility and protection, from AI-powered analytics to zero-trust architectures, with insights from Black Hat 2024 experts.

In an era where data is often referred to as the new oil, the ability to effectively manage, visualize, and protect this valuable resource has become paramount for organizations across all sectors. As the volume and complexity of data continue to grow exponentially, so do the challenges associated with securing it. At Black Hat 2024, industry leaders shared their perspectives on the critical role of data visibility and protection in modern cybersecurity. This article explores these challenges and solutions, drawing on insights from Jim Hyman of Ordr, Amer Deeba of Normalyze, and Steve Stone of Rubrik.

The Data Visibility Challenge

Jim Hyman, CEO of Ordr, emphasizes the scale of the data visibility challenge: "The explosive growth of assets such as devices, users, SaaS applications, and cloud workloads, in both volume and diversity, have made it impossible for security professionals to make decisions quickly." This complexity is further compounded by the rapid adoption of Internet of Things (IoT) devices, operational technology (OT), and the Internet of Medical Things (IoMT) in various industries.

Hyman points out a critical blind spot in many organizations: "Most asset management solutions are blind to IoT, OT, and IoMT devices - which comprise 40% of the assets in a network." This lack of visibility creates significant security risks, as organizations cannot protect what they can't see or don't know exists.

The Challenges of Data Management and Protection

1. Data Sprawl and Complexity

Amer Deeba, CEO and co-founder of Normalyze, highlights the complexity of modern data environments: "With the proliferation of diverse data environments (on-premises, cloud, SaaS, and PaaS platforms), it's important to remember that not all data should be treated equally." This diversity makes implementing consistent data protection measures across all platforms and environments challenging.

2. Shadow IT and Unauthorized Data Usage

The rise of cloud services and remote work has increased shadow IT, where employees use unauthorized applications or services to handle company data. Deeba notes, "Amid the ongoing discussions about AI's risks, a crucial yet often overlooked challenge for security professionals is the unpredictable ways in which AI can be misused."

He provides a stark example: "An employee who, using an AI tool, stumbled upon a confidential HR layoff list simply by querying documents with his name. This incident underscores a significant pain point: the gap in understanding and managing AI's reach within the organization."

3. Data Classification and Prioritization

Not all data is created equal, and organizations struggle to classify and prioritize their data for protection effectively. Steve Stone, Head of Rubrik Zero Labs, emphasizes the importance of this process: "It's critical to have visibility into what data is most sensitive and where it resides. Make resilience the centerpiece of the organization's strategy, and, in executing that, begin at the beginning: Figure out what the most critical data is."

4. Compliance and Regulatory Requirements

With the increasing focus on data privacy regulations like GDPR, CCPA, and others, organizations face the challenge of ensuring their data management and protection practices comply with complex requirements. This includes data sovereignty issues in multi-cloud environments and the need for robust audit trails.

5. Insider Threats and Privileged Access

Insider threats, whether malicious or accidental, pose a significant risk to data security. Deeba points out, "IT teams need to distinguish between different types of data and their respective security requirements to implement targeted security measures effectively."

Strategies for Enhancing Data Visibility and Protection

1. Implement Comprehensive Asset Discovery and Management

Hyman advocates for a holistic approach to asset management: "Ordr provides complete visibility and business insights for every network-connected asset, including devices, users, installed software, SaaS applications, and cloud workloads - and it does so with AI-driven analytics."

Key aspects of this approach include:

• Continuous discovery and inventory of all assets

• Real-time monitoring of device behavior and communications

• AI-driven analytics to identify anomalies and potential threats

2. Adopt a Data-Centric Security Model

Deeba emphasizes the need for a shift in focus: "Developers and security professionals need to pivot their focus towards a deep understanding of the data they create and manage. This understanding is crucial for implementing targeted security measures."

A data-centric security model involves:

• Classifying data based on sensitivity and business impact

• Implementing appropriate security controls based on data classification

• Continuously monitoring data access and usage patterns

3. Leverage AI and Machine Learning for Data Protection

All three experts highlight the potential of AI and ML in enhancing data visibility and protection. Stone mentions, "At Black Hat, Rubrik and Mandiant, part of Google Cloud, announced a new partnership and integration designed to fortify customers' threat detection capabilities and cyber recovery."

AI and ML can be used to:

• Automate data discovery and classification

• Detect anomalous data access patterns

• Enhance threat intelligence and incident response capabilities

4. Implement Zero Trust Architecture

A zero-trust approach is crucial in today's distributed data environments. This involves:

• Verifying every access request, regardless of source

• Implementing least privilege access principles

• Continuously monitoring and logging all data access activities

5. Enhance Data Resilience and Recovery Capabilities

Stone emphasizes the importance of data resilience: "Prepare for a contested recovery by ensuring backups are fully immutable and available, automating as much of the recovery process as possible, and testing recovery outcomes across hybrid environments."

Key aspects of data resilience include:

• Implementing immutable backups

• Developing and testing comprehensive disaster recovery plans

• Ensuring the ability to recover quickly from ransomware and other cyber attacks

6. Foster a Culture of Data Stewardship

Deeba stresses the importance of organizational culture in data protection: "By embedding data visibility and control into the development process, we can better protect against breaches and misuse."

To build a culture of data stewardship:

• Provide regular training on data handling and security best practices

• Implement clear policies and procedures for data access and usage

• Encourage open communication about data-related risks and incidents

7. Implement Continuous Monitoring and Improvement

Continuous monitoring and improvement are essential, given the dynamic nature of data environments and threat landscapes. Hyman notes that Ordr's platform addresses critical enterprise asset use cases, including "Threat/anomaly detection and response" and "Compliance and audit reporting."

Continuous monitoring should include:

• Real-time tracking of data movement and access

• Regular security assessments and penetration testing

• Ongoing updates to security policies and procedures based on emerging threats and technologies

Emerging Trends and Future Directions

1. AI-Powered Data Protection

Deeba highlights Normalyze's approach to AI in data security: "We enable organizations to confidently integrate AI by ensuring sensitive data is not inadvertently exposed or misused. Our enhanced sensitive data detection capabilities for AI applications built with public cloud providers, open-source, and commercial APIs ensure data remains protected."

2. Automated Compliance and Governance

As regulatory requirements become more complex, automated solutions for compliance and governance are gaining traction. Deeba mentions, "Our solutions include MIP integration and automated enforcement of data security policies, ensuring that data remains compliant with industry regulations."

3. Integration of Security and Data Management

Stone points out the trend towards integrating security and data management functions: "Rubrik and Mandiant will bring together their respective Ransomware Response and Incident Response teams to provide joint customers with additional investigative and recovery support in the face of cyberattacks."

4. Focus on Data Access Governance

Deeba notes an essential development in this area: "We just announced the launch of our native app for Snowflake, which allows IT teams to give the right level of permission to access data based on user roles and responsibilities."

Conclusion: A Call for Proactive Data Management

As organizations continue to grapple with the challenges of data visibility and protection, it's clear that a proactive, comprehensive approach is necessary. The insights shared by Jim Hyman, Amer Deeba, and Steve Stone at Black Hat 2024 highlight the need for integrated solutions that address the full spectrum of data management, visibility, and protection challenges.

For developers, engineers, and security professionals, the key takeaways are:

1. Implement comprehensive asset discovery and management across all environments

2. Adopt a data-centric security model with robust classification and prioritization

3. Leverage AI and ML for enhanced data protection and threat detection

4. Implement zero trust principles in data access and management

5. Enhance data resilience and recovery capabilities

6. Foster a culture of data stewardship throughout the organization

7. Implement continuous monitoring and improvement of data security measures

By embracing these strategies and staying attuned to emerging trends, organizations can build a strong foundation for cybersecurity in an increasingly data-driven world. As Deeba aptly puts it, "The real opportunity lies in developing robust AI oversight mechanisms that not only mitigate risks but also harness AI's potential to enhance security protocols."

The future of cybersecurity lies in our ability to manage, visualize, and protect our data assets effectively. By prioritizing data visibility and protection, organizations can not only enhance their security posture but also unlock the full potential of their data in driving innovation and business growth.