Let's kick things off with some hard facts: Each year, more than a billion people’s data is compromised due to cybersecurity breaches. In fact, February 2025 alone saw multiple high-profile incidents, including a $1.46 billion crypto heist. Combine the rise of AI-powered attacks and the increasing sophistication of ransomware with the fact that companies are running on the most complex cloud environments ever, and it’s clear that organizations need a future-proof data security strategy.
In this article, we’ll take an in-depth look at the data security landscape in 2025, covering everything from fundamental concepts to practical implementation strategies. But first:
Cloud Data Security Snapshot 2025
From exposed VMs to over-privileged buckets, the Snapshot quantifies today’s top cloud data threats—download the free report.
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What is data security?
Data security refers to the techniques, tools, and policies that keep digital information safe from unauthorized access, corruption, theft, or misuse—whether it resides in cloud infrastructure, SaaS environments, or on-prem systems.
In 2025, data security extends far beyond perimeter firewalls and disk encryption. It now requires real-time monitoring, least-privilege access controls, automated discovery of sensitive data, and contextual risk analysis across hybrid and multi-cloud environments.
Modern data security strategies protect not only personal information (like PII and PHI), but also source code, cloud configurations, and machine-generated logs—all of which are prime targets for attackers.
Common data security threats in 2025
From cloud misconfigurations to AI-powered phishing, today’s threats exploit fragmented environments and over-permissioned access. Below are some of the most pressing risks facing organizations in 2025—and why traditional perimeter defenses are no longer enough.
Advanced persistent threats (APTs)
Data breaches remain the poster child for security nightmares, and attackers are only getting bolder. Whether it’s a misconfigured cloud storage bucket, an exposed database, or a zero-day exploit, threat actors are laser-focused on exfiltrating sensitive data at scale.
Not your run-of-the-mill attacks, APTs are carefully planned campaigns that can last months or even years, but what makes them particularly nasty is how they blend in with normal network traffic. APT groups’ tools are designed specifically to bypass your unique security setup, which makes them incredibly hard to spot. And when these tools are in place, they map out your entire digital environment. Before making a move, attackers have already identified your key data and know who has access to what.
Ransomware and data destruction
Ransomware has evolved from simple lock-and-extort to full-blown business disruption. Attackers now use “triple extortion,” where they encrypt your data, steal it, and then threaten to leak or destroy it if you don’t pay up.
If that wasn’t enough, Ransomware-as-a-Service (RaaS) platforms make it easy for less technically skilled threat actors to launch devastating attacks by paying to use ready-made ransomware kits.
Insider threats
When it comes to insider threats, disgruntled employees aren’t your only concern. Sometimes it’s accidental: Someone mistakenly uploads sensitive data to a public repo or shares credentials over Slack. Other times, it is deliberate data theft or sabotage. Either way, the impact can be catastrophic, especially when insiders hold overprivileged accounts.
Credential leaks and privilege escalation
Cybercriminals continue to favor credential theft as a leading method for gaining unauthorized access. Phishing, brute force, and credential stuffing attacks are rampant, and with AI-powered phishing lures, even seasoned users get duped. Once attackers get a foothold, privilege escalation is what comes next. They start to move laterally, harvest more credentials, and ultimately access sensitive data or critical systems.
Shadow IT and uncontrolled SaaS
Lately, shadow IT has started to explode as teams spin up SaaS tools and cloud resources outside official channels. Unvetted AI tools, unsanctioned file-sharing apps, and rogue cloud instances all expand the attack surface.
Regulatory non-compliance
On top of everything else, regulatory compliance is getting harder and harder. Failing to secure data can mean more than just a breach—it can trigger hefty fines and reputational fallout. Next, let’s take a closer look at how compliance fits into the data security picture.
How data security relates to compliance
Data security and compliance are now more tightly linked than ever. Regulations like GDPR, HIPAA, and PCI DSS have set strict requirements for protecting personal and sensitive data.
You can also find frameworks such as NIST CSF and ISO 27001 guiding organizations on data risk management and security controls. But it’s worth noting that meeting these standards isn’t just about avoiding fines. In the long run, strong data security builds trust and keeps your business competitive as privacy laws and industry mandates continue to evolve.
At the end of the day, good security practices lay the groundwork for a solid compliance posture, making audits and regulatory checks much smoother.
Types of data that need protection
It’s not just customer records or payment info that need safeguarding anymore. We’re now dealing with a sprawling attack surface, and you’ll find attackers are just as interested in your source code, configuration files, and even the logs your systems spit out. Here are the main categories of data that demand your attention:
Personally identifiable information (PII)
PII covers any information capable of pinpointing someone’s identity. Think names, email addresses, government IDs, phone numbers, or even biometric data. Organizations must know where PII lives, who has access, and how it’s secured—especially as global regulations tighten.
Protected health information (PHI)
Healthcare data is in a league of its own. PHI covers medical records, health insurance details, lab results, and anything else tied to a patient’s identity.
Proposed updates to the HIPAA Security Rule are set to raise the bar, making encryption, multi-factor authentication, and regular vulnerability scans essential for anyone handling PHI. These requirements are expected to come into force in 2026.
Payment data
If your systems handle credit card numbers, bank details, or payment tokens, PCI DSS 4.0 is the rulebook you need to follow. Role-based access, unique user IDs, and strong authentication are all table stakes for protecting payment data.
Intellectual property and source code
Attackers target code repositories and build pipelines, looking for secrets, vulnerabilities, or even opportunities to inject malicious code. To protect intellectual property, lock down access, monitor for suspicious activity, and ensure that only trusted personnel can touch your most sensitive projects.
Configuration files, secrets, and credentials
APIs, cloud keys, and environment variables together compose the skeleton keys to your infrastructure. Exposed secrets can lead to full-blown breaches, lateral movement, and data exfiltration. Some key data security best practices are to treat secrets as sensitive data, store them in secure vaults, and rotate them regularly. Automated scanning for hardcoded credentials in codebases is a great start.
Machine-generated data and logs
Logs and telemetry data often contain sensitive information such as user activity, error traces, and even fragments of PII or PHI. All attackers know this and often rely on unsecured log stores as a starting point.
Core principles of data security
The real foundation of cloud data security is a set of tried-and-true principles that, when applied consistently, help organizations stay ahead of attackers and compliance auditors alike:
Data discovery and classification: You can’t protect what you don’t know you have. That’s why data discovery is the first step in any robust security program. Next, classification—where you tag data by sensitivity to prioritize protection efforts—comes into play.
Encryption strategies: With solid encryption, your information stays indecipherable to outsiders lacking the correct decryption keys. This year, standards like AES-256 for storage and TLS 1.3 for network traffic are key.
Access control and least privilege: Access control is about making sure only the right people (and services) can touch sensitive data. Role-based access control (RBAC), multi-factor authentication (MFA), and the principle of least privilege are your best friends here.
Data loss prevention (DLP): DLP tools help you spot and block risky data movements. Think accidental uploads to public buckets or unauthorized downloads of customer records.
Monitoring and visibility: With continuous monitoring, you gain up-to-the-minute visibility into data access patterns across your environment.
Data lifecycle management
From creation to archiving (and eventual deletion), every stage of the data lifecycle needs protection. That’s why it’s critical to automate retention policies, securely delete what you no longer need, and ensure backups are encrypted and tested regularly.
Security by design
Integrate data security controls into your development pipelines using infrastructure as code (IaC) to enforce secure defaults and run regular risk assessments. For example, use IaC templates that enforce encrypted-by-default storage buckets or deny public access to sensitive services. The DevSecOps mindset, which emphasizes that security is everyone’s job, helps catch issues early and keeps your defenses agile.
The Data Security Best Practices [Cheat Sheet]
No time to sift through lengthy guides? Our Data Security Best Practices Cheat Sheet condenses expert-recommended tips into a handy, easy-to-use format. Get clear, actionable advice to secure your cloud data in minutes.
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Cloud data security
Cloud environments today are sprawling, dynamic, and deeply interconnected. That means securing data is no longer just about where it lives—but how it's accessed, who can reach it, and whether there are exposure paths attackers could exploit. Even a single misconfigured identity or exposed key can lead to full-blown compromise.
Luckily, there are solutions. A key technology here is data security posture management (DSPM). DSPM continuously discovers sensitive data across cloud environments, maps access paths and configurations, and identifies real-world risks—like exposed storage buckets or over-permissioned identities.
DSPM solutions help prioritize remediations based on blast radius and context, giving teams a faster path to protecting what matters most.
Data security for AI and machine learning
On one hand, AI and ML have supercharged threat detection and automated responses. On the other hand, they’ve given attackers brand-new ways to breach systems.
Data poisoning attacks, where attackers manipulate training data to skew AI predictions, are on the rise. So are model inversion attacks, which involve attackers reconstructing sensitive training data just by querying the model, exposing everything from medical records to proprietary business info. Generative AI systems can also leak confidential data if sensitive information is used in prompts or training. Incidents like the Samsung code leak and the accidental exposure of terabytes of research data show the real-world impact of these risks.
But AI data security means more than just locking down data. It’s about controlling access to models, sanitizing training inputs, using synthetic or anonymized data, and continuously monitoring for abnormal behavior. It’s tough and a whole new world, but robust data governance, explainable AI frameworks, and regular audits are now non-negotiable.
Unified cloud data security with Wiz
Managing data across multiple clouds, SaaS tools, and ephemeral resources requires a unified security approach. That’s where Wiz comes in. Wiz offers agentless data discovery, graph-based risk prioritization, and runtime monitoring—all in one platform.
Agentless, multi-cloud data discovery and data classification: Wiz DSPM uses agentless scanning to discover and classify sensitive data across AWS, Azure, GCP, SaaS, and even code repositories.
Contextual exposure-path mapping with the Wiz Security Graph: Wiz’s Security Graph can visually map how sensitive data is exposed by correlating identity permissions, network reachability, and infrastructure misconfigurations. Imagine being able to trace how a leaked key in a CI/CD pipeline could be used to reach sensitive PII in an S3 bucket—with all the lateral movement paths in between.
Risk prioritization based on real exploitability: Unlike tools that drown you in alerts, Wiz combines vulnerability, identity, and network context to prioritize risks that are truly exploitable in your environment.
Automated least-privilege enforcement: Wiz analyzes effective permissions across all your cloud identities and resources, surfacing excessive access paths.
Continuous runtime threat detection and compliance automation: Wiz continuously monitors all access to your data for suspicious runtime behaviors and emerging AI-related risks.
Parting thoughts
In 2025, the focus of data security should be continuous discovery, risk-based controls, and real-time monitoring. It’s time to move beyond bare-minimum compliance to true resilience. A multi-layered, context-aware approach is now the key to defending against evolving attacks and avoiding costly breaches.
See how Wiz can help your team discover, protect, and monitor sensitive data across your entire cloud estate—before it becomes a target.
👉 Book a demo to see how Wiz helps you discover, protect, and monitor sensitive data—before it’s exploited.
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