Most enterprises today store and process vast amounts of data from various sources within a centralized repository known as a data warehouse or data lake, where they can analyze it with advanced analytics tools to generate critical business insights. 

Modern data warehouse platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, and IBM Db2 are built with strong security measures to ensure that no prying eyes can glimpse the information held within. But although these platforms are secure, that doesn’t mean the data is safe, for organizations are exposed to significant risks when storing information there in the first place. 

These risks stem from the fact that data in transit is inherently vulnerable, as it leaves the system where it was originally housed, embarking on its journey to our centralized data warehouse. 

When we talk about data in transit, what we’re talking about is the millions of daily journeys made by bytes of information as they zip from one device or system to another, across both private networks and the world wide web. Data in transit can be imagined as an army of tiny messengers that race along the fiber optic cables that make up the backbone of today’s networks. These messengers all carry valuable bits of information that need to be analyzed, including sales information, corporate secrets, financial transactions, user logins, customer behavior, credit card information, and personal details. 

Unlike data at rest, which refers to data sitting securely in a cloud data lake, warehouse, server, or elsewhere, data in transit is information that’s on the move. As such, it’s compelled to leave the protection of the cybersecurity software and firewalls that ring most data repositories, making it much more vulnerable.  

The significance of protecting data in transit cannot be understated. The information handled by data analytics teams is often among the most sensitive – critical for enabling organizations to identify trends and customer behavior patterns to inform their business strategies. Securing this data is a colossal responsibility and it needs to be done right. 

Why Must We Secure Data in Transit? 

The more information that’s flowing across a company’s network, the more interested hackers become in trying to steal it. Networks today are littered with potential threats lurking at every corner. The strategy of cyberattackers involves first breaking into the network, often by compromising someone’s log-in details through a phishing attack, before moving laterally across the network. While looking for vulnerabilities in our applications and databases, they’re also hunting for any interesting data in transit they might be able to get their hands on. These threats are automated and persistent, forever on the prowl, searching for anything they can exploit. 

The risks of data in transit can be broadly divided into two categories, or hurdles, that each of our little data messengers must overcome.

First is the data read threat, which is when sensitive data in transit is read by a bad actor when it’s sent from one device to another. Second is the data change threat, which is when data in transit is intercepted by an attacker and altered in some way before it reaches its intended destination. A variant on this is when an attacker creates new data and makes it appear as if it was sent by a trusted source. 

In either case, it’s essential to prevent these kinds of attacks, as bad data makes for bad data analytics insights, not to mention potential damage to your organization. Overcoming these hurdles requires not only some clever solutions but also the realization that data in transit is both vulnerable and valuable. It cannot be allowed to fall into the wrong hands. 

Safeguarding Data in Transit

The go-to solution for protecting data in transit is encryption, which acts like a cloaking device that keeps our little messengers safe as they traverse the network. Encryption is a technique that enables data to be scrambled and made unintelligible, so only the person with the correct key – the intended recipient – can decode it to make sense of it. 

When data in transit is encrypted, it becomes useless to hackers, even if they do manage to intercept it. Whatever information they steal will be utter gibberish. But it’s important to understand that organizations must use the right kind of encryption, the most powerful stuff, such as the Advanced Encryption Standard (AES) or Transport Layer Security (TLS). 

Encryption keeps data in transit secure against data reads and data change attacks, because it means the information cannot be read by any attackers as it’s traveling from A to B. However, encryption alone will not prevent data origin attacks, which require additional measures to be put in place. 

To secure data in transit against these threats, the primary solutions are authentication tokens and security certificates that validate who sent the data, and virtual private networks (VPNs) that keep communications entirely secret. These techniques are usually applied in combination with data encryption. 

Authentication can be thought of as a secret handshake that confirms the messenger is who they claim to be. It ensures that the data coming into a data repository is from the person or entity that’s identified as the sender. 

As for VPNs, these are like secret paths that create a secure channel through which information can be sent, enabling data in transit to be funneled from where it was created to a data warehouse without anyone else knowing. It’s the stealthiest trick in the book. 

Using Analytics to Identify Threats

Thanks to advances in artificial intelligence and machine learning, there has been a lot of focus on real-time threat analysis in recent years, which provides data analytics teams with a more proactive way to identify threats to data in transit. By leveraging ML and AI tools, teams can keep tabs on their little messengers as they make their way across the network and ensure they are kept safe from harm. 

One key strategy involves behavioral analysis, where AI algorithms analyze network traffic to establish a baseline of normal behavior. This makes it easier for teams to detect any deviations from the norm, which might indicate that their messengers are under attack. 

AI and ML algorithms can also enable proactive threat detection that involves scanning networks and endpoints for any threats lying in wait. Such tools work by analyzing data in transit as it travels across the network, looking for signs of suspicious activity that might indicate unauthorized access has been gained. 

This continuous monitoring of data in transit has emerged to become one of the chief security strategies of modern organizations today, enabling them to respond to threats the moment they occur. By doing this, they can make sure that the data that ends up being analyzed hasn’t been compromised. 

Layering Up

By applying the above techniques to data in transit, data teams and security professionals can build strong safeguards against the major threats it faces as it makes its way to data repositories. But it’s important to remember that security is an ongoing battle, with bad actors always on the lookout for new tricks and techniques they can use to overcome even the most elaborate defenses. 

To ensure the integrity of data in transit, the best strategy is to layer up with a more sophisticated security strategy. For the most sensitive data, that means using strong encryption to scramble it, a demand for authentication, and a stealthy route across the network that avoids the gaze of hackers. On top of that, teams should consider implementing tools to monitor data in transit in real time as a further guarantee. After all, the vast majority of modern networks rarely catch a break, as many organizations have an insatiable need for fresh data that’s analyzed around the clock.