Samer Diya, the Vice President of META at Forcepoint, says a well-implemented data security solution can provide the necessary visibility, accountability, and protection to safeguard sensitive data, even in the challenging environment of remote work

In your experience, how can companies leverage data governance automation to improve data security for remote workers?
Automating data governance is incredibly powerful for companies looking to enhance data security. By leveraging automation, organisations can streamline workflows, establish clear accountability, and enable efficient data security practices, even for a remote workforce.

The orchestration capabilities of solutions such as Forcepoint DSPM not only streamline workflows and establish clear data ownership but also make the process efficient and convenient. The solution allows organisations to personalise workflows based on data risk severity, ensuring targeted and appropriate responses to data security situations. For example, publicly exposed files can be swiftly relocated to secure internal repositories, and unnecessary files can be deleted to minimise potential risks. Improved communication and a well-oiled machine for data governance are the results of defined responsibilities.

Furthermore, solutions such as Forcepoint DSPM offer a comprehensive view and actionable insights, providing organisations with a deep understanding of their data landscape, including data stored by remote employees. This intuitive dashboard gives organisations a sense of being well-informed, with insights into data risks such as over-exposed files and redundant or outdated data. This centralised approach enables automating data governance workflows and helps businesses make informed decisions, ensuring data security regardless of location.

Solutions such as Forcepoint DSPM empower organisations to bolster data security for their remote workforce. This comprehensive strategy minimises risks, enhances operational efficiency, and ensures consistent compliance with regulations.

What are some key considerations when implementing automated data governance solutions for a remote workforce?
There are several key considerations to keep in mind to ensure effective data security and automation capabilities:

1. Comprehensive Data Discovery and Classification: Initiate the process by employing automated data discovery and classification tools, such as Forcepoint DSPM. This critical step provides visibility into sensitive data across diverse environments, including cloud, on-premises, and remote work setups. Understanding the data landscape is essential for applying appropriate security controls.
2. Streamlined Workflows and Clear Accountability: Aim for solutions that facilitate clear ownership and responsibilities for data handling. Well-defined roles ensure accountability and enhance workflow efficiency, even with a dispersed remote workforce.
3. Customised Alerts and Remediation: Ensure that the ability to create customised workflows is based on risk assessment of data. Set specific criteria for notifications and remediation actions, such as securely relocating exposed files or deleting redundant data. This targeted approach minimises the impact of data security incidents, ensuring the protection of remote workers’ data.
4. Regular Monitoring and Anomaly Detection: Implement regular monitoring of data access and usage patterns, especially in remote work scenarios. Real-time anomaly detection capabilities help identify and respond to suspicious activities, preventing potential data breaches and ensuring compliance.
5. Seamless Integration with Existing Security Measures: Ensure that your chosen solution to support automated data governance integrates smoothly with your organisation’s current security tools and infrastructure. This unified approach strengthens your overall data protection strategy and simplifies security management for remote workers.

By addressing these important considerations, organisations can enhance data security, ensure compliance, and empower their remote workforce to operate productively and securely. A well-implemented data security solution can provide the necessary visibility, accountability, and protection to safeguard sensitive data, even in the challenging environment of remote work.

How can automation help address challenges like data access control and data classification in a remote work environment?
While humans continue to play a critical role, strategic implementation of data security automation can save time and improve accuracy, freeing up security teams to focus on high-priority problems and pursue more of a big-picture approach. Leveraging technologies that automate challenges like data access control and data classification can play a pivotal role in the following:

1. Improved Data Discovery and Visibility: Solutions such as Forcepoint DSPM rapidly scan and identify sensitive data across diverse environments, including cloud, on-premises, and remote work setups. This comprehensive visibility provides a clear understanding of your data landscape, enabling the application of appropriate security controls, even with a dispersed remote workforce.
2. Automated Remediation and Incident Response: Automation shines in triggering predefined remediation actions, such as securely relocating exposed files or revoking access, in response to data security incidents or policy violations. This rapid and consistent response mitigates the impact of data breaches or compliance issues, providing remote workers with swift and effective solutions.
3. Continuous Monitoring and Anomaly Detection: Automated solutions continuously monitor data access and usage patterns, identifying anomalies that may indicate security threats or data misuse. This proactive security approach safeguards sensitive information in the remote work setting, ensuring a vigilant eye over data access.

By embracing automation for data discovery, classification, access control, and incident response, organisations can effectively tackle the unique challenges of data security in a remote work environment. The comprehensive approach enhances visibility, streamlines workflows, and fosters an agile and responsive data protection strategy.

What are some potential challenges associated with relying on AI for data security automation, and how can these be mitigated?
When relying on AI for data security automation, several potential challenges may arise, each requiring careful consideration and strategic mitigation. The dependence of AI systems on data quality is a critical factor. Inaccurate, incomplete, or biased data can undermine the accuracy and effectiveness of AI-driven security measures. Ensuring data quality is maintained through robust data governance practices, including data cleansing, validation, and enrichment, is essential. By investing in data quality management, organisations can improve the reliability and performance of AI-driven security solutions.

Cybersecurity risks targeting AI systems are a prominent challenge. AI systems are vulnerable to cyberattacks, such as data poisoning or model theft, which can compromise system integrity. Many tools can collect, store and process large amounts of data from various sources – including user prompts. If an employee inadvertently discloses sensitive information through a seemingly innocuous prompt, the AI tools could leak that data while answering the prompts of users outside the organisation – not only exposing the organisation to third-party risks but also amplifying the potential for data leaks.

To address this, robust cybersecurity measures specifically designed to protect AI systems are crucial. These measures include secure development practices, strong authentication, access control, and data protection capabilities like leveraging cloud-native DLP solutions. Additionally, regular security assessments help identify vulnerabilities and strengthen the overall security posture of AI systems, where ethical and legal considerations also come into play.

The use of AI in data security raises concerns about privacy invasion, the potential for malicious use, and the need to comply with regulatory frameworks. Developing comprehensive ethical and legal frameworks is essential to guide the responsible development and deployment of AI-driven security solutions. Organisations should actively engage in regulatory compliance and foster a culture prioritising ethical standards and privacy protection. While AI can reduce human error, excessive reliance on automation may lead to a lack of human involvement. Continuous training and awareness programs help operators understand AI’s limitations and foster vigilant human-AI collaboration.