As the business world continues to embrace different phases of “Digital” and evolves into being heavily reliant on data, data platforms have become prime targets for cybercriminals, leveraging the vast reservoirs of sensitive information housed in their databases. As these repositories store vast amounts of data, they become vulnerable to attacks that can compromise data integrity, disrupt operations, and lead to significant financial and reputational damage. The complexity and interconnectedness of these systems also mean that a single point of vulnerability can have widespread implications.

Cyber attacks can manifest in various forms, jeopardizing the very foundation of data-driven decision-making processes. This article navigates the escalating cyber threat landscape. It provides a comprehensive exploration of how modern data quality tools, like digna with its AI-powered data quality platform, offer a proactive solution to detect early signs of cyber threats within your databases.

Use Cases of digna in Early Cyber Threat Detection

Unexpected Data Deletion

Data deletion can be a strong indicator of an intending cyber attack and this can go unnoticed if you handle a large amount of data that can’t be manually validated on the go.  

Imagine discovering that a million records in your database have suddenly dropped to 900,000, or even worse, the table is empty! Such drastic changes are clear indicators of potential cyber-attacks, possibly aiming to sabotage your data integrity. 

digna's vigilant real-time monitoring system promptly alerts you to these types of anomalies, allowing for swift action to investigate and mitigate potential security breaches before they have a significant impact. 

Data Manipulation Detection

Cyber attackers may surreptitiously modify or alter data within the database, leading to misleading insights and compromised decision-making. Subtle alterations can go unnoticed, potentially resulting in flawed analytics, incorrect reporting, and misguided business decisions. Common real-life examples of data manipulation include; 

• A sudden doubling in the amount of payments for all transactions can signify unauthorized manipulation. digna's alert system catches these irregularities, flagging them for immediate review.

  
  

• Whether it's a column where the count of missing values spikes or the surname of every customer appears hashed, indicating encryption or alteration, digna detects these changes. It's especially adept at noticing when the average length of data in a column changes, mostly increased is a subtle sign often overlooked but potentially indicative of tampering.

Denial-of-Service (DoS) Attack Indicators

Overwhelming the data platform with traffic disrupts normal operations, leading to service unavailability. DoS attacks can result in downtime, loss of productivity, and hinder the availability of critical data.

A significant uptick in access count within a short period can signal a DoS attack, intended to overwhelm and incapacitate your database. digna's alerting mechanism ensures you're promptly informed, enabling you to counteract the flood of access requests before they escalate into a full-blown service disruption.

Unauthorized Data Exportation

One of the prominent reasons for hackers targeting your database would be to steal sensitive customer data. digna protects your database by alerting you of any unauthorized data exported. This is possible when there is;

• Upload Volume Changes: An unusual increase in data upload volume might indicate that a hacker is exporting data from your database. 

  
  

• SQL Query Spikes: An unexpected rise in the count of executed SQL queries can also be a telltale sign of a data breach. 

digna's monitoring capabilities are designed to catch and alert such anomalies, providing an early warning system against data exfiltration attempts.

How digna Early Detection Works

Autometrics

digna continuously profiles your data, capturing essential metrics that serve as the baseline for normal operations.

Forecasting Model

Employing unsupervised machine learning algorithms, digna predicts future metrics based on historical data, setting the stage for early anomaly detection.

Autothresholds

The AI algorithms adjust threshold values dynamically, honing the sensitivity of the system to alert on deviations that could signify potential threats.

Dashboard

digna's intuitive dashboard offers a real-time overview of your data health, making it easier to monitor and respond to alerts.

Notifications

Instant notifications ensure that you're the first to know about any detected anomalies, streamlining the process of taking preventive or corrective action.

As cyber threats continue to loom large over data warehouses, lakes, and lakehouses, the role of data quality tools in cybersecurity becomes increasingly vital. digna stands at the forefront of modern data quality (MDQ) and security solutions, offering a sophisticated, AI-driven platform capable of detecting early signs of cyber threats in databases. 

By integrating digna with your database system, you can safeguard your data against the evolving landscape of cyber threats, ensuring that your operations remain uninterrupted and your data integrity uncompromised. 

With digna, peace of mind comes from knowing that your databases are continuously monitored and protected against potential cyber-attacks, making it an essential tool in the arsenal of any data-driven organization.

Watch Our Demo or contact us today to discuss your peculiar data quality issues and develop a strategy that safeguards your data and maintains its integrity. 

Record counts, access spikes and query volumes are the kind of platform-level signals covered by data platform observability with digna.

Frequently asked questions

Can data quality monitoring detect cyber attacks?

Yes, according to the post, unusual changes in data can be early signs of an attack. digna monitors signals such as record counts, missing values, access counts and executed SQL queries, and alerts you when they deviate from normal, so you can investigate before a breach has significant impact.

What are signs of data manipulation in a database?

The post gives concrete examples: payment amounts suddenly doubling across all transactions, a spike in the count of missing values in a column, every customer surname appearing hashed, and a change, usually an increase, in the average length of data in a column, a subtle sign often overlooked.

How can unexpected data deletion indicate a cyber attack?

A sudden drop in records can signal sabotage. The article's example is a table of one million records falling to 900,000, or becoming empty altogether. Such changes are hard to spot manually at scale, so digna's real-time monitoring alerts you and lets you investigate quickly.

How do you detect data exfiltration from a database?

Watch for two warning signs named in the post: an unusual increase in data upload volume, which may mean a hacker is exporting data, and an unexpected rise in the count of executed SQL queries. digna alerts on both anomalies as an early warning against data exfiltration attempts.

How does digna detect anomalies early?

It combines five components: autometrics that profile data to set a baseline, a forecasting model using unsupervised machine learning to predict future metrics, autothresholds that adjust dynamically, a real-time dashboard of data health, and instant notifications so you are the first to know about detected anomalies.