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Cyber Awareness Month - Top Tips for Protecting Data: Learn how context-aware AI and modern governance can strengthen data protection, reduce risks, and simplify compliance in today's dynamic digital landscape.
Businesses today are drowning in data. With everyone working from anywhere, the cloud taking over, and BYOD policies running rampant, data is spread out, moving quickly, and constantly changing.
In the meantime, cyberattacks are getting smarter and faster. Collaboration tools have made sharing data extremely easy, and even Gen AI is leaking data. As if that's not enough, compliance requirements are constantly evolving. With limited budgets, understaffing, and expansive skills gaps, it's no wonder IT and security teams feel overwhelmed.

Operationalizing data security has been a long-standing challenge for decades. Despite costly investments and countless hours of labor, admins are still flying blind. Legacy data security tools that require regex, trainable classifiers, or other pattern-based methods catch only a small fraction of sensitive data and bury IT teams in false positives.
The good news is that there are new, modern data security governance platforms available today that have ditched the legacy approach. In particular, businesses should seek solutions that leverage context-aware AI for discovery, risk monitoring, and remediation that can deliver the following benefits:
Superior visibility into their data: In order to effectively protect sensitive data, organizations first need to know precisely what data they have, where it's hiding, who's peeking at it, and how it's being shared.

Context-aware AI scans each data record in its entirety and can not only locate personally identifiable information (PII) and payment card information (PCI) but can even find things like intellectual property (IP) and other critical business records that other tools miss since this data doesn't usually contain patterns. Additionally, AI can identify duplicate or near-duplicate data, as well as the category and subcategory of each record. For example, it knows the difference between a bank statement and a corporate tax form or a resume versus a job application. Having this level of granularity enables security teams to make better-informed decisions when assigning classification labels, establishing where data should be located, or setting access and retention policies.

Stopped sensitive data leakage: Not only must security teams make sure that employees and third-party contractors aren't accessing data that they shouldn't, but they also must ensure that the users who are authorized to access it aren't sharing it. They should seek a solution that helps them 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.

Enabled GenAI without expanding the attack surface: GenAI is reshaping our world in real time. Tools like Microsoft Copilot, ChatGPT, Perplexity, and Google Gemini are changing how we make decisions, solve challenges, create content, and engage with others at work and home. But while they bring greater operational efficiencies, improved decision-making, and reduced 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 define guardrails on what type of data should be blocked or redacted by groups and for each GenAI application and help them curate data when training their own proprietary GenAI workloads.

Aced regulatory compliance audits: Regulatory frameworks help businesses mitigate risks, operationalize processes, and maintain customer trust. But mapping security controls to these frameworks can quickly feel overwhelming. Adding further complexity, different industries and regions can have widely varying data handling and classification requirements. Businesses need a clear view of their compliance status, tools to fix issues, and peace of mind that they're not one audit from disaster. They should seek a solution that provides a dashboard showing their current compliance status with all relevant regulations and security controls, as well as support for custom frameworks. They also need granular visibility into all data records that violate compliance, with the ability to remediate them directly within the platform.

Maximized effectiveness of existing 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 only accessible by authorized individuals. Faster ROI, smarter policies, and less stress: Context-aware AI significantly speeds up the data discovery process and saves countless hours that administrators used to spend on algorithm tuning and chasing false positives. However, since new data is constantly generated and is always changing, capturing only a snapshot of the data at a single point in time is not enough.

Security teams can save time and enhance 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 reduce the burden on overstretched security teams by providing data security experts to assist with tasks ranging from deployment to training their teams on the platform, building a data governance roadmap, mapping classification labels, reporting, and tracking ongoing progress toward their objectives.

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This release was published on openPR.