Don’t Let Data Breaches Block Your AI Innovation: Secure DataOps with BigQuery

Your company can’t innovate without secure and efficient data operations (DataOps). This is especially true in light of recent data breaches, such as the one at Microsoft and Snowflake. On June 17th, the hacking group UNC5537 accessed the accounts of around 165 Snowflake customers and stole valuable data. They are now reportedly demanding ransom payments ranging from $300,000 to $5 million from up to 10 compromised companies.

The Importance of Secure Data in AI Innovation

Data is the primary fuel for AI innovation. Without secure data operations, the integrity of your data—and thus the reliability of your AI models—can be compromised. Inaccurate, stolen, or manipulated data can lead to faulty AI predictions and decisions, undermining the very innovation you’re striving to achieve. By ensuring robust security measures with BigQuery, you protect your data’s integrity, enabling accurate and reliable AI outcomes that drive your business forward.

What is BigQuery?

BigQuery is Google’s fully managed, serverless data warehouse. It can churn through terabytes of data in seconds, enabling ultra-fast SQL queries by leveraging the immense processing power of Google’s infrastructure. It’s designed to make large-scale data analysis accessible, efficient, and secure. BigQuery is a powerful tool for any organisation that values deriving insights for smarter decision-making.

BigQuery’s Multiple Layers of Security

External Threat Protection

Encryption at Rest and in Transit
BigQuery ensures data security through robust encryption. All your data is encrypted both at rest and in transit, using advanced encryption standards, or AES-256 (a highly secure encryption algorithm that uses a 256-bit key to turn your plain text or data into an unreadable format). This means that whether your data is stored in BigQuery or being transferred across networks, it remains protected from unauthorised access.

Identity and Access Management (IAM)
BigQuery’s IAM features provide detailed control over who can access your data. By setting specific roles and permissions, your organisation can ensure that only authorised personnel can view or modify sensitive information. This granular level of control helps minimise the risk of external breaches and unauthorised access.

VPC Service Controls
To further enhance security, BigQuery integrates with Virtual Private Cloud (VPC) Service Controls, which provide a way to define a security perimeter around BigQuery datasets. This protects data from being exfiltrated by malicious insiders or external actors.

Internal Threat Protection

Column and Row Level Security
BigQuery is a robust foundation for your internal threat protection strategy by offering granular access control through column and row-level security. Imagine data labelled with “tags” that act like permission slips. These tags restrict access to sensitive information based on user groups. Row-level security adds another layer by filtering data users can see based on their attributes. This double protection makes it much harder for someone inside to misuse sensitive data.

Data Loss Prevention (DLP)
Google’s DLP API is integrated with BigQuery to automatically detect and protect sensitive information, such as personal identifiable information (PII), within your datasets. Regularly scanning your datasets in BigQuery can help prevent accidental data leaks.

Access Transparency
BigQuery offers Access Transparency logs, which provide visibility into Google employees’ actions and the rationale behind them. This transparency ensures that any access to your data by Google’s support staff is justified and documented, building trust and accountability. Configure your alerts for any unusual access patterns to quickly address potential security issues and maintain accountability.

Audit Logging
Comprehensive audit logging in BigQuery tracks all activities within your datasets, including data access and modifications. These logs are important for monitoring suspicious activities and ensuring compliance with regulatory requirements by providing an auditable trail of data interactions.

Big Query vs. Snowflake

Real-World Applications

Financial Services
ATB Financial transformed its data operations using BigQuery, achieving faster insights and significant cost savings. BigQuery enabled real-time data ingestion, advanced analytics, and improved security, reducing job execution time from hours to seconds. This modernisation facilitated better decision-making, enhanced data governance, and substantial efficiency gains. Financial institutions can similarly benefit by adopting BigQuery for secure, efficient, and agile data management, positioning themselves for future growth and innovation.

Retail Industry
At Mercado Libre, integrating Google BigQuery has revolutionised their operations by enabling real-time analytics, which supports rapid decision-making and fraud detection. This integration helped them achieve 99% SLA availability for critical processes and optimise delivery schedules. Other retailers can benefit by adopting BigQuery to analyse transactional data, enhance customer interactions, and streamline operations through data-driven insights.

Invest in Secure DataOps

A full GCP Cloud Security Posture Review with BigQuery can take as little as 23 hours, including setup, agent installation, and customer workshops. Alternatively, there is a simpler option from Google that is not counted as Google Cloud Professional Services Organisation (PSO) that leverages the Google Cloud Platform Cloud Foundation Toolkit scorecard—an easy-to-use tool that provides a set of basic security rules and policies for your cloud environment. It’s easier, with basic security rules and policies, and can be done in less than one week.

This is a small investment for the substantial benefits of enhanced security and operational efficiency, especially when data breaches can have devastating consequences. Google Cloud Platform with BigQuery offers unparalleled security features that protect your data from both external and internal threats, similar to the recent breaches at Snowflaw and Microsft. It’s time to embrace your company’s future of Secure DataOps.

Ready to transform your data operations with BigQuery? Fill out the form below today to start the conversation and learn how you can ensure your data is protected and optimised for success.