Data breaches keep rising even as companies pour more money into cybersecurity. The root of the problem? Many organizations lack a robust system to prevent sensitive data from falling into the wrong hands.
Well-developed data protection systems work on multiple fronts – they tag and classify data, automatically block and fix potential leaks, and maintain clear visibility over how data moves throughout the organization. Most critically, they zero in on and address the most significant security risks facing the company.
Companies face different data security risks depending on their business, but certain challenges appear repeatedly across organizations. Let’s look at some of these common threats.
Insider Threats
The shift to remote and hybrid work, combined with growing cloud usage, has made it harder to protect against insider threats because employees now have more ways to access and potentially steal sensitive data, creating a larger security gap that organizations need to defend.
Generative AI (GenAI) tools have also opened up a new security risk: employees might accidentally paste confidential information into these systems without realizing the consequences (more on this subsequently). The point here is that tackling insider threats requires looking at the problem from multiple angles.
Businesses need strong security rules, strict controls over who can access what, and constant monitoring of user behavior (in a non-intrusive manner, of course). Also, training employees on security risks is vital since many breaches happen through careless mistakes or compromised accounts. And when employees leave, teams must follow detailed procedures to revoke their access and check that no data walks out the door with them.
To mitigate against insider threats, modern data protection systems need to harness AI and behavior analysis to spot suspicious activity as it happens. By staying ahead of threats and looking at the big picture, companies stand a better chance of stopping insiders from walking away with sensitive data.
Cloud Adoption
Traditional Data Loss Prevention (DLP) solutions, primarily designed for on-premises environments, often struggle to effectively address the complexities of cloud data security. Today, sensitive information flows freely between different cloud services and apps. So, businesses need more flexible, dynamic solutions to handle this new situation.
Due to this, organizations migrating to the cloud may face challenges recreating existing on-premises DLP policies and ensuring consistent data protection across hybrid environments. Not to mention how the proliferation of cloud/SaaS applications, each with unique security configurations and access controls, further complicates DLP efforts.
When companies move to the cloud, they often struggle to carry over their existing data protection rules from their local servers. Making things worse, there’s now a maze of cloud apps and services to secure, each with its own security settings and controls. Trying to maintain consistent data protection across both worlds is challenging for IT teams.
More so, these security teams already have their hands full, managing data across multiple cloud platforms, managing data access across multiple cloud platforms, ensuring consistent data classification, and preventing unauthorized data sharing. And it’s not always clear who’s responsible for what when it comes to security between the company and cloud providers, leaving potential weak spots.
Getting cloud data protection right means looking at everything: people, processes, and technology. The best solutions are those built specifically for the cloud, with modern features like smart data labeling, behavior tracking, real-time monitoring, and more. These tools make it easier to see what’s happening with your data and enforce security rules consistently.
Generative AI
GenAI is opening up exciting possibilities for businesses to innovate and boost productivity. But there’s a catch – the same features that make these tools so powerful (their ability to absorb and learn from massive amounts of data) also make them risky. Sensitive information can slip through in ways that traditional data protection tools weren’t designed to catch.
We’ve already mentioned users who, unaware of the potential risks, inadvertently input confidential data into generative AI tools. Users often input confidential information, such as financial records, source code, or customer data, into these systems without fully understanding how that data is stored, processed, and potentially shared.
This risk is exacerbated by the fact that generative AI providers are not fully transparent about the storage and use of user data. Without such needed visibility, it’s difficult for organizations to assess and mitigate potential data leakage risks sufficiently. More so, many generative AI platforms are being run by third-party providers, which adds another layer of complexity to data governance and control.
Moreover, the ease with which generative AI tools can manipulate and synthesize data creates new challenges for identifying and preventing data exfiltration. Malicious actors could leverage these tools to create synthetic data that closely resembles real sensitive information, bypassing traditional DLP rules that rely on pattern matching and keyword detection.
To address these challenges, organizations are establishing clear policies governing the use of generative AI tools among their employees and implementing robust access controls to limit the exposure of sensitive data. Also, DLP strategies must shift from relying solely on static rules to incorporating more dynamic and context-aware approaches.
Digital/Data Transformation
The past couple of decades have been a period of massive digital transformation efforts, focusing on data, not just in terms of migration to cloud environments but also in adopting new data processing technologies and the increased need for data analytics. Unfortunately, data transformation processes can inadvertently introduce security gaps if not adequately managed.
Adopting technologies such as big data analytics and machine learning adds further complexity to DLP efforts because they often require third-party data processing services, which can be necessary for data transformation initiatives. More so, restructuring and reformatting data during transformation can render traditional rule-based DLP approaches ineffective.
Organizations need a strategic and coordinated effort to mitigate DLP risks associated with data transformation effectively. For one, data security considerations must be integrated throughout the data transformation cycle, from planning and design to implementation and ongoing monitoring.
Specifically, security leaders must engage with stakeholders across various business units involved in data transformation processes to ensure a collaborative and proactive approach to DLP. With an adaptable strategy, organizations can harness the power of data while minimizing risks to sensitive information.
Conclusion
A mature DLP program that is resilient to risks provides a proactive and adaptable defense against data loss. It enables organizations to embrace digital transformation initiatives confidently while minimizing the inherent risks to sensitive information. Investing in identifying and mitigating DLP risks is crucial for safeguarding the business.
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