Over the past two years, the pressure on businesses to digitally transform, meet changing customer expectations and implement more agile processes has reached a breaking point. While organizations recognize the value of cloud adoption to combat these issues, it doesn’t come without its challenges. There are often concerns around the security of the cloud and uncertainty about data protection and privacy. Similarly, privacy concerns around the Internet of Things (IoT) continue to cause issues in the industry. In manufacturing, for instance, new IoT devices are converging with process machinery that was never originally intended to connect to a broader network. Inherently, the security of these devices is far weaker than the modern alternatives.
To ensure protection in the cloud and IoT, deep observability into every part of the infrastructure is key. This includes a clear view of SSL/TLS encrypted traffic and TLS 1.3 encrypted flows. Yet, what does this mean for data privacy? Can organizations gain observability into every asset and across entire networks while also complying with data privacy regulations?
Why Deep Observability Matters to Security
When embarking on digital transformation initiatives, organizations should be asking several critical questions: how do we ensure everyone can use the network securely; how do we migrate to the cloud while meeting compliance and security controls; how can we identify and mitigate security or network anomalies? If these considerations are not addressed, it will slow the migration of workloads to the cloud or the integration of IoT devices, leaving a company vulnerable to breaches.
Deep observability is essential for bolstering security in the IT world and for operational technology (OT). Without a clear line of sight into all devices, it is impossible to monitor (or protect) what you cannot see. Ransomware attacks are rife today because cyber-criminals can easily penetrate a network and covertly gather intelligence for months before deploying any malware. This technique is why blind spots represent such a security risk; cyber-criminals are often present for weeks without being spotted. Therefore, gaps in visibility need to be eliminated. The key is finding a tool that can provide deep observability and actionable analytics while also prioritizing data privacy.
The Importance of Data-Masking
SSL/TLS encryption is commonly used by enterprises as a central pillar of their cybersecurity strategy, especially as most businesses are now working partially or fully in the cloud. However, this security measure has been turned on its head in recent years. At the same time, encryption previously existed to protect data from bad actors. It is now often leveraged by these criminals to hold a company’s own data at ransom or even conceal malicious activity. In fact, in 2021, it was found that more than 90% of malware was hidden in encrypted traffic. Given the amount of encrypted data traveling across networks and the risk it now poses, organizations need a way to efficiently decrypt SSL traffic, share it with tools and then re-encrypt it.
However, UK organizations must comply with the General Data Protection Regulation (GDPR), which recognizes that certain data must be processed/stored to maintain confidentiality. Compliance with these rules is essential for every industry, while confidentiality is crucial for public and private sectors. Regulations in industries like healthcare and finance are particularly stringent and require that sensitive data be protected, with penalties severe for non-compliance. This is where data masking comes in. Data masking effectively modifies sensitive data, so while it is structurally similar, it is of no use or value to those unauthorized to see it. This technique means that data is obscured before it is sent to security and monitoring tools, and therefore compliance becomes far easier as the sensitive data is never seen, processed or stored. It also protects NetOps teams from being exposed to confidential data inadvertently, and it adds a level of data protection if monitoring or analysis functions are outsourced.
Data-masking tools are an essential part of network intelligence solutions and must be implemented in order to ensure organizations can comply with GDPR and data privacy regulations while simultaneously protecting themselves from cybercrime. While SecOps teams can only protect their network from what they can see, privacy and security cannot be at odds with each other. Instead, these elements must work in harmony to ensure data is always safe while allowing digital transformation initiatives to succeed.