Businesses today are drowning in data. It’s growing faster than your inbox on a Monday morning — and the storage costs are just as relentless. With everyone working from anywhere, the cloud taking over, and BYOD policies running rampant, data is spread out, moving quickly, and constantly changing.
Meanwhile, cyberattacks are becoming smarter, faster, and sneakier. Collaboration tools have made sharing data very easy. Even Gen AI is revealing secrets. As if that’s not enough, compliance requirements are constantly evolving. Just when organizations think they got it figured out, there’s a new regulation — or five — to juggle.
Operationalizing data security has been a long-standing challenge for decades. Despite expensive investments and countless hours of effort, admins are still flying blind. Legacy data security tools that rely on regex, trainable classifiers, or other pattern-based methods only detect a small portion of sensitive data and bury your team in false positives.
IT and security teams are essentially superheroes… but even superheroes need a break. With limited budgets, understaffed teams, and expansive skills gaps, it’s no wonder things feel overwhelming.
The good news is that modern data security governance platforms are now available that have abandoned the legacy approach. Specifically, businesses should look for solutions that use context-aware AI for discovery, risk monitoring, and remediation, which can provide the following outcomes:
Gain better visibility into their data: To effectively protect sensitive information, organizations first need to know exactly what data they have, where it’s stored, who’s accessing it, and how it’s being shared.
Context-aware AI scans each data record in its entirety and can not only identify personally identifiable information (PII) and payment card information (PCI) but also detect items like intellectual property (IP) and other critical business records that other tools may miss. Additionally, it can recognize duplicate or near-duplicate data, as well as determine the category and subcategory of each record. For example, it understands the difference between a bank statement and a corporate tax form or a resume versus a job application. This level of granularity enables security teams to make more informed decisions when assigning classification labels, determining where data should be stored, or establishing access and retention policies.
Prevent sensitive data leaks: Security teams must ensure that employees and third-party contractors do not access data they shouldn’t, and they must also confirm that authorized users are not sharing it. They need a solution that allows them to contextually discover, monitor, and protect their sensitive data—not just at rest, but also as it travels to ensure that it isn’t being shared with unauthorized users, personal email addresses, file sharing applications, social media, or GenAI applications.
Enable GenAI without expanding their attack surface: Generative artificial intelligence (GenAI) is reshaping our world in real time. Tools like Microsoft Copilot, ChatGPT, Perplexity, and Google Gemini are changing how we make decisions, solve problems, create content, and interact at work or home. While they offer greater operational efficiency, better decision-making, and lower costs, they also introduce significant data security risks.
Organizations need a solution that helps them identify when employees are using unsanctioned or “shadow” GenAI so they can regain control and keep their data secure. They also need to ensure that, regardless of where their data is located, it is accessed by the correct identities, at the appropriate times, and for the intended purposes. A truly comprehensive data security governance solution will enable them to set guardrails on what type of data should be blocked or redacted by groups and for each GenAI application, and assist them in curating data when training their own proprietary GenAI workloads.
Excel in regulatory compliance audits: Regulatory frameworks assist businesses in reducing risks, implementing processes, and sustaining customer trust. However, mapping security controls to these frameworks can quickly become overwhelming. An added complication is that different industries and regions may have significantly different data handling and classification requirements. Businesses need a clear overview of their compliance status, tools to resolve issues, and peace of mind that they’re not one audit from disaster. They should look for a solution that offers a dashboard displaying their current compliance status with all relevant regulations and security controls, as well as support for custom frameworks. Additionally, they need granular visibility into all data records that violate compliance, with the ability to remediate them directly within the platform.
Enhance the effectiveness of their current security tools: Tools like zero trust network access (ZTNA) and cloud access security broker (CASB) don’t scan data to decide whether to allow or block access. Instead, they enforce policies based on labels, so if those labels are wrong or missing, they could either leak sensitive information to unauthorized users or block access needed for productivity. Context-aware AI and autonomous classification help ensure that sensitive data is labeled correctly and remains accessible only to authorized individuals.
Experience faster ROI, smarter policies, and less stress: Context-aware AI significantly accelerates the data discovery process and saves countless hours that administrators previously spent on algorithm tuning and chasing false positives. However, since new data is constantly generated and always changing, capturing only a snapshot of the data at a single point in time is not enough. Security teams can save time and improve data protection by implementing a solution that continuously monitors data, flags risks, and automates remediation steps. Choosing a provider that offers managed services can also lessen the burden on overstretched security teams by providing data security experts to help with tasks ranging from deployment and training their teams on the platform, to building a data governance roadmap, mapping classification labels, reporting, and tracking ongoing progress toward their goals.
