All Resources
In this article:
minus iconplus icon
Share the Blog

Rising to the Challenge of Data Security Leadership

Min Read

Any attempt to perfectly prescribe exactly what you need to build an effective data security role or team is a fool’s errand. There are simply too many variables you need to take into account - the size of the organization, the amount of data it has, the type of data that needs to be secured, the organization’s culture and risk appetite- all of these need to be weighed and balanced.

However, with that disclaimer and caveat in place, I do think there are some broad best practices that apply to almost every data security role, and those are the ones I want to focus on in this blog. 

Know Your Inputs and Restrictions - and Document them

Every data security team has a certain set of ‘inputs’ and restrictions under whose framework they need to operate. These can be regulatory frameworks like GDPR and CCPA, but they also include agreements with customers and partners and the level of risk the company is willing to accept. 

These inputs exist for every data security role. And the first thing you need to do when stepping into a data security position is to document these inputs and ensure that everyone’s on the same page. This isn’t the type of project that can be done by a single person or even a single team. Legal needs to be involved. Privacy needs to be involved. Security needs to be involved. The scope of this varies by company, but the main point is that there needs to be a governance arm telling you what the requirements and policies are before you can get to work enforcing anything.

It’s also important to remember that there are two different groups here. You have the leaders from the teams I mentioned. And then you have the engineers and executors that implement those policies. All the documentation in the world won’t help if there’s a communication breakdown between the deciders and the implementers. 

Managing Risk, Managing People

Whether you’re an individual or a team responsible for data security, it’s important to keep in mind the big picture - your answer can’t always be ‘no’ when asked ‘can I do this with our data’. Understand that there’s a business reason behind the question - and find a way to help them achieve their goals without violating the risk and legal parameters you’ve already established. 

The data security role also shouldn’t be responsible for actually going into the platforms to remediate issues. As far as possible, the actual remediation should be done by the teams that manage those platforms every day. If there’s 10 different data sources, the security team should be identifying those issues using data security tools. But they should also be - with minimal friction- dispatching the alerts, tasks, and remediation steps to the relevant teams. And the security team should be assisting these teams with developing, rolling out, and managing secure configurations so that, ideally, alerts and remediation tasks become less frequent over time.

Besides managing systems, there’s an enormous human component when it comes to data security success. (In general, I believe that most of our problems in security have a human dimension.) There are egos and authority on the line in discussions around data and how it should be used. The business side of the company may want to gather and retain as much data as possible. The privacy and legal teams may want as little as possible. Security leaders in general and particularly data security leaders will need to get along well with the heads of these various departments. They need to play the role of harmonizer between the competing demands and be able to get things done. This involves working with the peers of the CISO - head of legal, head of privacy, and making judgment calls in a space (data security)  that historically hasn’t had that much authority. Of course, that’s all changing now as every country and region adopts new data security regulations.

Managing up, down, and across the company is the main data security skill. It’s what helps separate  effective security leaders. Working well with engineers gets the data secured. Working well with legal, privacy, and compliance is the scaffolding that supports all of your effort. And like every security role, working well with the CISO is critical.

Data Security's a Great Career - Just Take Care Not to Burn Out

To wrap up, I’d say - there’s never been a better time to get into data security. The growth of regulations - and associated consequences for non compliance- means companies are investing in data security talent. For anyone looking to move from a general security or IT role into a data security role, a great first step is to improve your cloud and data skills. Understanding your company’s cloud environment, its different use cases, tools, and business objectives will give you the context you need to be successful in the role. It will help you understand the inputs and pressures on the different teams, and grow your perspective beyond just the technical part of the job.

The key to avoiding burnout is understanding the nature of the job. There’s always going to be a new tool, stakeholder, or regulation that you’re going to face. There’s no ‘finishing’ the work in any final sense. What you spent all month working on might be irrelevant overnight. That’s the game. And if it’s for you, I hope this blog helps in some small way think about what makes a successful data security professional.

Jason Chan is a security generalist with years of experience in system, network, and application security. Chan is the former VP of Information Security at Netflix.

Subscribe

Latest Blog Posts

Linoy Levy
Linoy Levy
March 10, 2026
4
Min Read

PDF Scanning for Data Security: Why You Can’t Treat PDFs as a Second-Class Citizen

PDF Scanning for Data Security: Why You Can’t Treat PDFs as a Second-Class Citizen

If you had to pick one file format that carries the bulk of your organization’s most sensitive documents, it would be PDF.

Contracts and NDAs, medical records, financial statements, invoices, tax forms, legal filings, HR packets - all of them default to PDF, and all of them tend to be copied, emailed, uploaded, and archived far beyond the systems where they originated. Adobe estimates there are trillions of PDFs in circulation; for most enterprises, a non‑trivial percentage of those live in cloud storage with overly broad access controls.

Despite that, many data security programs still treat PDF scanning as an afterthought. Tools that are perfectly happy parsing an email body or a CSV row suddenly become half‑blind when you hand them a complex multi‑page PDF,  and completely blind if that PDF is just a scanned image.

That is exactly the gap we set out to close with PDF scanning for data security in Sentra.

Why PDFs Are a First‑Class Data Security Risk

PDFs sit at the intersection of three uncomfortable truths:

  • They are the default format for high‑risk documents like contracts, patient records, tax filings, and financial reports.
  • They are easy to copy and spread - attached to emails, dropped into shared drives, uploaded to SaaS tools, and mirrored into backups.
  • They are often opaque to legacy DLP and discovery tools, especially when content is embedded in images or complex layouts.

From a risk perspective, treating PDFs as “less important than databases” makes no sense. If anything, the opposite is true: a single mis‑shared PDF can expose entire customer lists, PHI packets, or undisclosed financials in one move.

How Sentra Scans PDFs for Sensitive Data

Sentra’s PDF scanning is built on the same file parser framework we use for other unstructured formats, with specialized handling for both native text PDFs and image‑based PDFs. Our engine operates in two complementary modes.

Text Mode: Deep Inspection of Native PDF Content

In text mode, we extract all embedded text from each page and separately detect and pull out tables.

That distinction matters. In invoices, financial statements, and tax forms, the critical data often lives in rows and columns, not in narrative paragraphs. Sentra:

  • Detects table boundaries in PDFs.
  • Extracts cell values into a tabular representation.
  • Treats those cells as structured data, not just part of a flat text blob.

Once extracted, this structured view flows into Sentra’s classification engine, which analyzes it with specialized classifiers for:

  • PII such as names, email addresses, national IDs, and phone numbers.
  • Financial data such as account numbers, routing codes, and transaction details.
  • Regulated records such as tax identifiers or health‑related codes.

This approach is far more precise than a naive “search the whole document for 16‑digit numbers” method. It lets you distinguish, for example, between a random ID in the footer and a full set of cardholder details in an itemized table.

Image Mode: Solving the Scanned PDF Problem

A huge fraction of enterprise PDFs are actually just images of paper forms: patient intake sheets, signed contracts, faxed tax returns, screenshots dumped into PDF containers. To a legacy DLP engine, those documents are empty. To Sentra, they are just another OCR input.

Sentra:

  • Detects embedded images in PDF pages.
  • Extracts those images safely, including JPEG‑compressed content.
  • Processes them through our ML‑based OCR pipeline built on transformer‑style models.
  • Passes the resulting text into the same classifier stack we use for native text.

The result is that a scanned W‑2 receives the same depth of inspection as a digitally generated one. No practical difference, no exceptions.

Metadata, Encryption, and Hidden Exposure

Most tools stop at visible text. Sentra goes further.

PDF Metadata as a Data Source

PDF metadata can leak far more than people expect:

  • Author names and usernames
  • Internal file paths and system details
  • Document titles and descriptions that reference customers or projects

Sentra parses this metadata, normalizes it, and runs it through the same unstructured classification engine we use for body text and document context. That makes it possible to surface cases where you are unintentionally exposing sensitive details in fields that almost never get reviewed.

Encrypted and Password‑Protected PDFs

Password‑protected or encrypted PDFs are not invisible to Sentra. When our scanners encounter PDFs that cannot be opened for content inspection, we still:

  • Identify them as PDFs.
  • Record their location and basic properties.
  • Surface them in your inventory so you can see where opaque, potentially sensitive PDFs are accumulating, instead of silently skipping them.

In practice, a cluster of unreadable encrypted PDFs in an unexpected bucket is often a sign of data hoarding, shadow IT, or deliberate attempts to evade controls.

Security Architecture – Scanning Inside Your Cloud

All of this processing happens inside your cloud environment, using Sentra’s agentless, in‑cloud scanners rather than shipping PDFs out to a third‑party service. Our parser framework is designed around streaming and format‑aware readers, which means:

  • Files are processed as streams, not as long‑lived replicas.
  • PDF contents are analyzed in memory by the scanner, avoiding new long‑term copies in external systems.
  • The same engine powers analysis across databases, object storage, file systems, and SaaS sources.

The net effect is that Sentra reduces your blind spots around PDFs without turning the security solution itself into a new source of data exposure.

Regulatory Reality – PDFs Are Always in Scope

From a regulatory standpoint, PDFs are undeniably in scope. Frameworks and regulations such as:

  • GDPR for data subject rights, record‑keeping, and deletion
  • HIPAA for PHI in healthcare organizations
  • PCI DSS for cardholder data stored in receipts, statements, and chargeback files
  • SOX and other financial reporting controls

do not distinguish between data in databases and data in documents. A stack of PDFs in cloud storage, email archives, or shared drives counts just as much as a customer table in a production database when regulators and auditors review your posture. If your data security strategy covers only structured data and a narrow slice of text documents, you are leaving a disproportionate share of your most sensitive content unprotected.

Bringing PDFs into Your DSPM Strategy

PDFs are not going away. Digital‑first operations guarantee we will see more of them every year, not fewer. That makes them a natural priority for any serious Data Security Posture Management (DSPM) program.

Sentra’s PDF scanning is designed to make PDFs a first‑class citizen in your data security strategy:

  • Native text and scanned PDFs both receive full, ML‑powered inspection.
  • Tables and forms are treated as structured data for higher‑fidelity classification.
  • Metadata and unreadable encrypted PDFs are surfaced instead of ignored.
  • Everything runs inside your cloud, alongside support for 100+ other file formats.

You can explore how we extend the same approach across the rest of your data estate, or see it in action by requesting a demo.

<blogcta-big>

Read More
Nikki Ralston
Nikki Ralston
David Stuart
David Stuart
March 10, 2026
4
Min Read

How to Protect Sensitive Data in AWS

How to Protect Sensitive Data in AWS

Storing and processing sensitive data in the cloud introduces real risks, misconfigured buckets, over-permissive IAM roles, unencrypted databases, and logs that inadvertently capture PII. As cloud environments grow more complex in 2026, knowing how to protect sensitive data in AWS is a foundational requirement for any organization operating at scale. This guide breaks down the key AWS services, encryption strategies, and operational controls you need to build a layered defense around your most critical data assets.

How to Protect Sensitive Data in AWS (With Practical Examples)

Effective protection requires a layered, lifecycle-aware strategy. Here are the core controls to implement:

Field-Level and End-to-End Encryption

Rather than encrypting all data uniformly, use field-level encryption to target only sensitive fields, Social Security numbers, credit card details, while leaving non-sensitive data in plaintext. A practical approach: deploy Amazon CloudFront with a Lambda@Edge function that intercepts origin requests and encrypts designated JSON fields using RSA. AWS KMS manages the underlying keys, ensuring private keys stay secure and decryption is restricted to authorized services.

Encryption at Rest and in Transit

Enable default encryption on all storage assets, S3 buckets, EBS volumes, RDS databases. Use customer-managed keys (CMKs) in AWS KMS for granular control over key rotation and access policies. Enforce TLS across all service endpoints. Place databases in private subnets and restrict access through security groups, network ACLs, and VPC endpoints.

Strict IAM and Access Controls

Apply least privilege across all IAM roles. Use AWS IAM Access Analyzer to audit permissions and identify overly broad access. Where appropriate, integrate the AWS Encryption SDK with KMS for client-side encryption before data reaches any storage service.

Automated Compliance Enforcement

Use CloudFormation or Systems Manager to enforce encryption and access policies consistently. Centralize logging through CloudTrail and route findings to AWS Security Hub. This reduces the risk of shadow data and configuration drift that often leads to exposure.

What Is AWS Macie and How Does It Help Protect Sensitive Data?

AWS Macie is a managed security service that uses machine learning and pattern matching to discover, classify, and monitor sensitive data in Amazon S3. It continuously evaluates objects across your S3 inventory, detecting PII, financial data, PHI, and other regulated content without manual configuration per bucket.

Key capabilities:

  • Generates findings with sensitivity scores and contextual labels for risk-based prioritization
  • Integrates with AWS Security Hub and Amazon EventBridge for automated response workflows
  • Can trigger Lambda functions to restrict public access the moment sensitive data is detected
  • Provides continuous, auditable evidence of data discovery for GDPR, HIPAA, and PCI-DSS compliance

Understanding what sensitive data exposure looks like is the first step toward preventing it. Classifying data by sensitivity level lets you apply proportionate controls and limit blast radius if a breach occurs.

AWS Macie Pricing Breakdown

Macie offers a 30-day free trial covering up to 150 GB of automated discovery and bucket inventory. After that:

Component Cost
S3 bucket monitoring $0.10 per bucket/month (prorated daily), up to 10,000 buckets
Automated discovery $0.01 per 100,000 S3 objects/month + $1 per GB inspected beyond the first 1 GB
Targeted discovery jobs $1 per GB inspected; standard S3 GET/LIST request costs apply separately

For large environments, scope automated discovery to your highest-risk buckets first and use targeted jobs for periodic deep scans of lower-priority storage. This balances coverage with cost efficiency.

What Is AWS GuardDuty and How Does It Enhance Data Protection?

AWS GuardDuty is a managed threat detection service that continuously monitors CloudTrail events, VPC flow logs, and DNS logs. It uses machine learning, anomaly detection, and integrated threat intelligence to surface indicators of compromise.

What GuardDuty detects:

  • Unusual API calls and atypical S3 access patterns
  • Abnormal data exfiltration attempts
  • Compromised credentials
  • Multi-stage attack sequences correlated from isolated events

Findings and underlying log data are encrypted at rest using KMS and in transit via HTTPS. GuardDuty findings route to Security Hub or EventBridge for automated remediation, making it a key component of real-time data protection.

Using CloudWatch Data Protection Policies to Safeguard Sensitive Information

Applications frequently log more than intended, request payloads, error messages, and debug output can all contain sensitive data. CloudWatch Logs data protection policies automatically detect and mask sensitive information as log events are ingested, before storage.

How to Configure a Policy

  • Create a JSON-formatted data protection policy for a specific log group or at the account level
  • Specify data types to protect using over 100 managed data identifiers (SSNs, credit cards, emails, PHI)
  • The policy applies pattern matching and ML in real time to audit or mask detected data

Important Operational Considerations

  • Only users with the logs:Unmask IAM permission can view unmasked data
  • Encrypt log groups containing sensitive data using AWS KMS for an additional layer
  • Masking only applies to data ingested after a policy is active, existing log data remains unmasked
  • Set up alarms on the LogEventsWithFindings metric and route findings to S3 or Kinesis Data Firehose for audit trails

Implement data protection policies at the point of log group creation rather than retroactively, this is the single most common mistake teams make with CloudWatch masking.

How Sentra Extends AWS Data Protection with Full Visibility

Native AWS tools like Macie, GuardDuty, and CloudWatch provide strong point-in-time controls, but they don't give you a unified view of how sensitive data moves across accounts, services, and regions. This is where minimizing your data attack surface requires a purpose-built platform.

What Sentra adds:

  • Discovers and governs sensitive data at petabyte scale inside your own environment, data never leaves your control
  • Maps how sensitive data moves across AWS services and identifies shadow and redundant/obsolete/trivial (ROT) data
  • Enforces data-driven guardrails to prevent unauthorized AI access
  • Typically reduces cloud storage costs by ~20% by eliminating data sprawl

Knowing how to protect sensitive data in AWS means combining the right services, KMS for key management, Macie for S3 discovery, GuardDuty for threat detection, CloudWatch policies for log masking, with consistent access controls, encryption at every layer, and continuous monitoring. No single tool is sufficient. The organizations that get this right treat data protection as an ongoing operational discipline: audit IAM policies regularly, enforce encryption by default, classify data before it proliferates, and ensure your logging pipeline never exposes what it was meant to record.

<blogcta-big>

Read More
Nikki Ralston
Nikki Ralston
Romi Minin
Romi Minin
March 10, 2026
4
Min Read

How to Protect Sensitive Data in GCP

How to Protect Sensitive Data in GCP

Protecting sensitive data in Google Cloud Platform has become a critical priority for organizations navigating cloud security complexities in 2026. As enterprises migrate workloads and adopt AI-driven technologies, understanding how to protect sensitive data in GCP is essential for maintaining compliance, preventing breaches, and ensuring business continuity. Google Cloud offers a comprehensive suite of native security tools designed to discover, classify, and safeguard critical information assets.

Key GCP Data Protection Services You Should Use

Google Cloud Platform provides several core services specifically designed to protect sensitive data across your cloud environment:

  • Cloud Key Management Service (Cloud KMS) enables you to create, manage, and control cryptographic keys for both software-based and hardware-backed encryption. Customer-Managed Encryption Keys (CMEK) give you enhanced control over the encryption lifecycle, ensuring data at rest and in transit remains secured under your direct oversight.
  • Cloud Data Loss Prevention (DLP) API automatically scans data repositories to detect personally identifiable information (PII) and other regulated data types, then applies masking, redaction, or tokenization to minimize exposure risks.
  • Secret Manager provides a centralized, auditable solution for managing API keys, passwords, and certificates, keeping secrets separate from application code while enforcing strict access controls.
  • VPC Service Controls creates security perimeters around cloud resources, limiting data exfiltration even when accounts are compromised by containing sensitive data within defined trust boundaries.

Getting Started with Sensitive Data Protection in GCP

Implementing effective data protection begins with a clear strategy. Start by identifying and classifying your sensitive data using GCP's discovery and profiling tools available through the Cloud DLP API. These tools scan your resources and generate detailed profiles showing what types of sensitive information you're storing and where it resides.

Define the scope of protection needed based on your specific data types and regulatory requirements, whether handling healthcare records subject to HIPAA, financial data governed by PCI DSS, or personal information covered by GDPR. Configure your processing approach based on operational needs: use synchronous content inspection for immediate, in-memory processing, or asynchronous methods when scanning data in BigQuery or Cloud Storage.

Implement robust Identity and Access Management (IAM) practices with role-based access controls to ensure only authorized users can access sensitive data. Configure inspection jobs by selecting the infoTypes to scan for, setting up schedules, choosing appropriate processing methods, and determining where findings are stored.

Using Google DLP API to Discover and Classify Sensitive Data

The Google DLP API provides comprehensive capabilities for discovering, classifying, and protecting sensitive data across your GCP projects. Enable the DLP API in your Google Cloud project and configure it to scan data stored in Cloud Storage, BigQuery, and Datastore.

Inspection and Classification

Initiate inspection jobs either on demand using methods like InspectContent or CreateDlpJob, or schedule continuous monitoring using job triggers via CreateJobTrigger. The API automatically classifies detected content by matching data against predefined "info types" or custom criteria, assigning confidence scores to help you prioritize protection efforts. Reusable inspection templates enhance classification accuracy and consistency across multiple scans.

De-identification Techniques

Once sensitive data is identified, apply de-identification techniques to protect it:

  • Masking (obscuring parts of the data)
  • Redaction (completely removing sensitive segments)
  • Tokenization
  • Format-preserving encryption

These transformation techniques ensure that even if sensitive data is inadvertently exposed, it remains protected according to your organization's privacy and compliance requirements.

Preventing Data Loss in Google Cloud Environments

Preventing data loss requires a multi-layered approach combining discovery, inspection, transformation, and continuous monitoring. Begin with comprehensive data discovery using the DLP API to scan your data repositories. Define scan configurations specifying which resources and infoTypes to inspect and how frequently to perform scans. Leverage both synchronous and asynchronous inspection approaches. Synchronous methods provide immediate results using content.inspect requests, while asynchronous approaches using DlpJobs suit large-scale scanning operations. Apply transformation methods, including masking, redaction, tokenization, bucketing, and date shifting, to obfuscate sensitive details while maintaining data utility for legitimate business purposes.

Combine de-identification efforts with encryption for both data at rest and in transit. Embed DLP measures into your overall security framework by integrating with role-based access controls, audit logging, and continuous monitoring. Automate these practices using the Cloud DLP API to connect inspection results with other services for streamlined policy enforcement.

Applying Data Loss Prevention in Google Workspace for GCP Workloads

Organizations using both Google Workspace and GCP can create a unified security framework by extending DLP policies across both environments. In the Google Workspace Admin console, create custom rules that detect sensitive patterns in emails, documents, and other content. These policies trigger actions like blocking sharing, issuing warnings, or notifying administrators when sensitive content is detected.

Google Workspace DLP automatically inspects content within Gmail, Drive, and Docs for data patterns matching your DLP rules. Extend this protection to your GCP workloads by integrating with Cloud DLP, feeding findings from Google Workspace into Cloud Logging, Pub/Sub, or other GCP services. This creates a consistent detection and remediation framework across your entire cloud environment, ensuring data is safeguarded both at its source and as it flows into or is processed within your Google Cloud Platform workloads.

Enhancing GCP Data Protection with Advanced Security Platforms

While GCP's native security services provide robust foundational protection, many organizations require additional capabilities to address the complexities of modern cloud and AI environments. Sentra is a cloud-native data security platform that discovers and governs sensitive data at petabyte scale inside your own environment, ensuring data never leaves your control. The platform provides complete visibility into where sensitive data lives, how it moves, and who can access it, while enforcing strict data-driven guardrails.

Sentra's in-environment architecture maps how data moves and prevents unauthorized AI access, helping enterprises securely adopt AI technologies. The platform eliminates shadow and ROT (redundant, obsolete, trivial) data, which not only secures your organization for the AI era but typically reduces cloud storage costs by approximately 20 percent. Learn more about securing sensitive data in Google Cloud with advanced data security approaches.

Understanding GCP Sensitive Data Protection Pricing

GCP Sensitive Data Protection operates on a consumption-based, pay-as-you-go pricing model. Your costs reflect the actual amount of data you scan and process, as well as the number of operations performed. When estimating your budget, consider several key factors:

Cost Factor Impact on Pricing
Data Volume Primary cost driver; larger datasets or more frequent scans lead to higher bills
Operation Frequency Continuous scanning with detailed detection policies generates more processing activity
Feature Complexity Specific features and policies enabled can add to processing requirements
Associated Resources Network or storage fees may accumulate when data processing integrates with other services

To better manage spending, estimate your expected data volume and scan frequency upfront. Apply selective scanning or filtering techniques, such as scanning only changed data or using file filters to focus on high-risk repositories. Utilize Google's pricing calculator along with cost monitoring dashboards and budget alerts to track actual usage against projections. For organizations concerned about how sensitive cloud data gets exposed, investing in proper DLP configuration can prevent costly breaches that far exceed the operational costs of protection services.

Successfully protecting sensitive data in GCP requires a comprehensive approach combining native Google Cloud services with strategic implementation and ongoing governance. By leveraging Cloud KMS for encryption management, the Cloud DLP API for discovery and classification, Secret Manager for credential protection, and VPC Service Controls for network segmentation, organizations can build robust defenses against data exposure and loss.

The key to effective implementation lies in developing a clear data protection strategy, automating inspection and remediation workflows, and continuously monitoring your environment as it evolves. For organizations handling sensitive data at scale or preparing for AI adoption, exploring additional GCP security tools and advanced platforms can provide the comprehensive visibility and control needed to meet both security and compliance objectives. As cloud environments grow more complex in 2026 and beyond, understanding how to protect sensitive data in GCP remains an essential capability for maintaining trust, meeting regulatory requirements, and enabling secure innovation.

<blogcta-big>

Read More
Expert Data Security Insights Straight to Your Inbox
What Should I Do Now:
1

Get the latest GigaOm DSPM Radar report - see why Sentra was named a Leader and Fast Mover in data security. Download now and stay ahead on securing sensitive data.

2

Sign up for a demo and learn how Sentra’s data security platform can uncover hidden risks, simplify compliance, and safeguard your sensitive data.

3

Follow us on LinkedIn, X (Twitter), and YouTube for actionable expert insights on how to strengthen your data security, build a successful DSPM program, and more!

Before you go...

Get the Gartner Customers' Choice for DSPM Report

Read why 98% of users recommend Sentra.

White Gartner Peer Insights Customers' Choice 2025 badge with laurel leaves inside a speech bubble.