Sensitive Data Protection – Cloud DLP Features

Google Cloud Sensitive Data Protection (formerly Cloud DLP)

📢 Service Rebranding (2023): Cloud Data Loss Prevention (Cloud DLP) is now part of Sensitive Data Protection, a family of services designed to help discover, classify, and protect sensitive data. The API name remains the same: Cloud Data Loss Prevention API (DLP API).

  • Sensitive Data Protection (formerly Cloud DLP) is a fully managed service designed to help discover, classify, and protect the most sensitive data inside and outside Google Cloud.
  • Sensitive Data Protection helps inspect the data to gain valuable insights and make informed decisions to secure your data.
  • It effectively reduces data risk with de-identification methods like masking, tokenization, and encryption.
  • It seamlessly inspects and transforms structured and unstructured data.
  • Sensitive Data Protection is deeply integrated into Security Command Center Enterprise risk engine for continuous data monitoring, high-value asset identification, vulnerability analysis, and attack scenario simulation.
  • Supports data in Google Cloud (BigQuery, Cloud Storage, Cloud SQL, Datastore), multicloud (Amazon S3, Azure Blob Storage), and external sources via hybrid jobs.

Sensitive Data Protection Services

  • Sensitive Data Protection is a family of services comprising three core capabilities:
    • Discovery – Automatically discovers, classifies, and profiles data across organizations, folders, or projects.
    • Inspection – Detects and classifies sensitive data in content, storage, and images.
    • De-identification – Transforms sensitive data to reduce risk while retaining utility.

Sensitive Data Protection Discovery Service

  • The discovery service continuously and automatically discovers, classifies, and profiles data across an organization.
  • Discovery helps understand the location and nature of data, including shadow data that might not be subject to proper governance.
  • Discovery generates data profiles at project, table, and column levels providing:
    • Data classifications (infoTypes detected)
    • Sensitivity levels and data risk levels
    • Data size and shape
    • Data security posture
    • Estimated null proportion and uniqueness (at column level)
  • Supported Data Sources:
    • BigQuery (GA since April 2022) – including BigLake tables
    • Cloud SQL (GA since March 2024)
    • Cloud Storage (GA since June 2024)
    • Amazon S3 (Sept 2024) – requires Security Command Center Enterprise with AWS connector
    • Azure Blob Storage (April 2025) – requires Security Command Center Enterprise with Azure connector
    • Vertex AI datasets (GA Feb 2025) – profiles training and tuning data for sensitivity
    • Vertex AI tuning jobs (Feb 2026)
  • Discovery can automatically attach tags to resources based on calculated data sensitivity, enabling IAM conditions to grant or deny access based on data sensitivity.
  • Discovery can detect unencrypted secrets (passwords, authentication tokens) in Cloud Functions and Cloud Run environment variables, reporting findings to Security Command Center.
  • Discovery findings can be published to Security Command Center, Dataplex Universal Catalog (Knowledge Catalog), and BigQuery.
  • Supports subscription pricing mode for predictable costs regardless of data growth.
  • Scan configurations are manageable via Terraform (since June 2024).

Data Security Posture Management (DSPM)

  • DSPM provides a data-centric view of Google Cloud security, integrated with Security Command Center.
  • DSPM lets you continuously identify and reduce data risk by understanding what sensitive data you have, where it is stored, and if its use aligns with security and compliance requirements.
  • Starts with a data map providing a birds-eye view of data across your environment, its sensitivity level, and default security posture.
  • Sensitive Data Protection discovery feeds sensitivity signals into DSPM for default data risk assessment.

Sensitive Data Protection Actions

  • An action occurs after a Sensitive Data Protection job completes successfully or, in the case of emails, on error.
  • Supported action types:
    • Save findings to BigQuery (inspection and risk jobs)
    • Save findings to Cloud Storage (inspection jobs – added Aug 2025)
    • Publish to Pub/Sub (inspection and risk jobs)
    • Publish to Security Command Center (risk jobs and discovery)
    • Add Dataplex Catalog aspects based on insights from data profiles (replaced Data Catalog integration)
    • Publish to Google Cloud’s operations suite (risk jobs)
    • Notify by email (inspection and risk jobs)
    • Send inspection results to Dataplex Universal Catalog (Knowledge Catalog) as aspects (Sept 2025)

⚠️ Deprecated Action: “Publish to Data Catalog” was deprecated on September 30, 2025. Data Catalog was discontinued on January 30, 2026, replaced by Knowledge Catalog (formerly Dataplex Universal Catalog). Use “Add Dataplex Catalog aspects” instead.

Sensitive Data Protection Key Concepts

  • Classification is the process to inspect the data and know what data we have, how sensitive it is, and the likelihood.
  • De-identification is the process of removing identifying information from data.
  • De-identification techniques supported:
    • Redaction: Deletes all or part of a detected sensitive value.
    • Replacement: Replaces a detected sensitive value with a specified surrogate value.
    • Masking: Replaces a number of characters of a sensitive value with a specified surrogate character, such as a hash (#) or asterisk (*).
    • Pseudonymization/Tokenization: Replaces sensitive data values with cryptographically generated tokens. Supports:
      • CryptoReplaceFfxFpeConfig – Format-preserving encryption
      • CryptoDeterministicConfig – Deterministic encryption for consistent tokenization
      • CryptoHashConfig – One-way cryptographic hashing
    • Built-in Tokenization (Jan 2025): Serverless API endpoints for on-the-fly tokenization without managing third-party deployments, hardware, or VMs. Fully regionalized for compliance.
    • Generalization: Abstracts a distinguishing value into a more general, less distinguishing value to preserve data utility while reducing identifiability.
    • Bucketing: “Generalizes” a sensitive value by replacing it with a range of values. (For example, replacing a specific age with an age range.)
    • Date shifting: Shifts sensitive date values by a random amount of time.
    • Time extraction: Extracts or preserves specified portions of date and time values.
    • Dictionary replacement: Replaces each detected sensitive value with a random value from a provided word list.

Sensitive Data Protection InfoTypes

  • Sensitive Data Protection uses information types — or infoTypes — to define what it scans for.
  • An infoType is a type of sensitive data, such as a name, email address, telephone number, identification number, credit card number, and so on.
  • An infoType detector is the corresponding detection mechanism that matches an infoType’s matching criteria.
  • InfoType detectors are used in scan configurations to determine what to inspect for and how to transform findings.
  • Supported infoType detector types:
    • Built-in infoType detectors — specified by name, include country-specific and globally applicable data types. Categories include:
      • PII (names, addresses, phone numbers, dates of birth)
      • Financial (credit cards, bank accounts, tax IDs)
      • Credentials & secrets (API keys, passwords, auth tokens, encryption keys)
      • Medical data (medical records, health info)
      • Government IDs (passports, driver’s licenses, national IDs)
      • Document type classifiers (finance, legal, medical, R&D, source code)
      • Image/Object infoTypes (faces, signatures, passports, photo IDs, barcodes, license plates, persons, whiteboards)
      • Image safety classifiers (violence, sexually explicit/suggestive content)
    • General infoType detectors — broader categories (e.g., CREDIT_CARD_DATA, MEDICAL_DATA, GOVERNMENT_ID) that cover multiple specific infoTypes.
    • Custom infoType detectors, defined by you:
      • Small custom dictionary detectors — simple word lists, ideal for several tens of thousands of words or phrases.
      • Large custom dictionary detectors — generated from large lists stored in Cloud Storage or BigQuery, ideal for up to tens of millions of words.
      • Regular expressions (regex) detectors — detect matches based on regex patterns.
      • Custom metadata label detectors (March 2026) — detect specific client-provided metadata labels.
  • Inspection rules to fine-tune scan results:
    • Exclusion rules — decrease findings (including ExcludeByHotword for column-name-based exclusion and image-based exclusion rules).
    • Hotword rules — increase quantity or change likelihood of findings.
    • Adjustment rules (GA Feb 2026) — customize likelihood based on context, supporting both text and image operations.
  • InfoType versioning — control which detection model to use via InfoType.version: latest, stable, or legacy.
  • Likelihood levels used for confidence scoring:
    • LIKELIHOOD_UNSPECIFIED: Default value; same as POSSIBLE.
    • VERY_UNLIKELY
    • UNLIKELY
    • POSSIBLE
    • LIKELY
    • VERY_LIKELY

Classification and De-identification

  • Sensitive Data Protection can classify, redact, and de-identify sensitive data contained in text-based content, images, and content stored in Google Cloud storage repositories.
  • Text Classification and De-identification
    • Text Classification returns classification findings.
    • Text Redaction produces output with sensitive data removed using a placeholder.
    • Conversational content inspection (June 2026) — supports inspecting and de-identifying conversational content via the Conversation ContentItem type.
    • Batched content inspection (June 2026) — supports inspecting and de-identifying batched content via BatchContentItem.
  • Image Classification and Redaction
    • Uses Optical Character Recognition (OCR) technology to recognize text prior to classification.
    • Inspection — inspects base64-encoded images for specified infoTypes, returns detected InfoTypes with pixel coordinates and bounding boxes.
    • Redaction — masks sensitive data findings with opaque rectangles (configurable color).
    • Object detection and redaction (July 2025) — detects and redacts sensitive objects (barcodes, license plates, persons, whiteboards) in images.
  • Storage Classification
    • Scans data stored in Cloud Storage, Datastore, BigQuery, and Cloud SQL.
    • Supports scanning of binary, text, image, Microsoft Word, PDF, Apache Avro files, and archive files.
    • Detects sensitive data in headers and footers of rich document types.
    • Unrecognized file types are scanned as binary files.
  • Date Handling — if dates are considered PII:
    • Using generalization or bucketing (can reduce date utility)
    • Using date shifting which randomly shifts dates but preserves sequence and duration.

Re-identification Risk Analysis

  • Risk analysis is the process of analyzing sensitive data to find properties that might increase the risk of subjects being identified.
  • Risk analysis methods can be used before de-identification to help determine an effective strategy, or after de-identification to monitor for changes or outliers.
  • Supported risk metrics:
    • k-anonymity — measures whether individuals can be distinguished from at least k-1 other individuals.
    • l-diversity — extends k-anonymity by ensuring diversity of sensitive values within each equivalence class.
    • k-map estimation — estimates re-identification risk based on quasi-identifiers.
    • δ-presence (delta-presence) — risk metric for when membership in the dataset is itself sensitive.

Sensitive Data Protection Templates

  • Templates help decouple configuration information from the implementation of requests.
  • Templates provide a robust way to manage large-scale rollouts.
  • Two types of templates:
    • Inspection Templates: Templates for saving configuration for inspection scan jobs, including what predefined or custom detectors to use.
    • De-identification Templates: Templates for saving configuration for de-identification jobs, including infoType and structured dataset transformations.
  • Discovery scans can be configured to reprofile data when the inspection template changes (since Feb 2024).

AI and Generative AI Protection

  • Sensitive Data Protection extends to AI workloads to help organizations safely unlock data value at every stage of the AI journey.
  • Vertex AI Discovery (GA Feb 2025) — discovers and profiles sensitivity of training and tuning data in Vertex AI datasets.
  • Context-aware inspection — understands data context even within images and rich documents for effective AI agent data access control.
  • Fine-grained data minimization for:
    • AI model training and fine-tuning data preparation
    • Runtime protection for chat, data collection, and generative AI prompts/responses
    • Ensuring adherence to regulations and internal policies
  • Integrated with AI Protection framework for security posture management of AI workloads.

Integration with Security Command Center

  • Sensitive Data Protection is deeply integrated into Security Command Center Enterprise risk engine.
  • Publishes the following finding types:
    • Data sensitivity and Data risk — calculated sensitivity and risk levels of profiled tables.
    • PUBLIC_SENSITIVE_DATA — publicly accessible resources with sensitive data.
    • SECRETS_IN_STORAGE — secrets detected in environment variables.
    • SENSITIVE_DATA_CMEK_DISABLED — sensitive data without customer-managed encryption.
  • Security Command Center can automatically prioritize resources based on data sensitivity.
  • Discovery is included in Security Command Center Enterprise tier (since Nov 2024).

Regional Endpoints and Data Residency

  • Regional endpoints (GA Aug 2024) keep data at rest, in use, and in transit within a specified region.
  • Available in 35+ regions globally including multi-regions (asia, europe, us).
  • Content methods process data synchronously in memory — data is not stored on Google Cloud.
  • Storage methods process data in the same region where it resides.

Compliance and Security

  • Certifications: ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018, PCI DSS, HIPAA BAA, MTCS SS 584.
  • Supports GDPR compliance with built-in and custom infoType detectors applicable to EU data.
  • Data encryption in transit and at rest follows Google Cloud encryption standards.

GCP Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • GCP services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • GCP exam questions are not updated to keep up the pace with GCP updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. A healthcare company needs to de-identify patient records stored in BigQuery before sharing them with researchers. They need to preserve the format of Social Security numbers for application compatibility while making them non-reversible without the key. Which de-identification technique should they use?
    1. Masking
    2. Redaction
    3. Format-preserving encryption (CryptoReplaceFfxFpeConfig)
    4. Date shifting
    Show Answer

    Answer: c – Format-preserving encryption maintains the format while encrypting the value.

  2. An organization wants to continuously monitor all their BigQuery, Cloud Storage, and Cloud SQL data across all projects for sensitive data without setting up individual scan jobs. What should they use?
    1. Sensitive Data Protection inspection jobs with scheduled triggers
    2. Sensitive Data Protection discovery service at the organization level
    3. Cloud DLP content inspection API
    4. Security Command Center vulnerability scanning
    Show Answer

    Answer: b – The discovery service automatically and continuously discovers, classifies, and profiles data across the organization.

  3. A company is building a generative AI application and needs to ensure that sensitive PII is not included in prompts sent to their LLM. Which Sensitive Data Protection capability should they use?
    1. Discovery service profiling
    2. Risk analysis jobs
    3. Content inspection and de-identification API
    4. Storage classification jobs
    Show Answer

    Answer: c – Content inspection and de-identification processes data synchronously and can be used at runtime to inspect and redact sensitive data from AI prompts and responses.

  4. A financial institution wants to automatically restrict access to BigQuery tables containing highly sensitive data. Which Sensitive Data Protection feature should they configure?
    1. De-identification templates
    2. Discovery with automatic sensitivity-based tagging and IAM conditions
    3. Inspection templates with Pub/Sub actions
    4. Risk analysis jobs with email notifications
    Show Answer

    Answer: b – Discovery can automatically attach tags based on data sensitivity, and IAM conditions can grant or deny access based on these tags.

  5. A security team needs to discover if any Cloud Functions or Cloud Run services have unencrypted secrets in their environment variables. Which feature should they enable?
    1. Storage inspection jobs targeting Cloud Functions
    2. Sensitive Data Protection secrets discovery with Security Command Center integration
    3. Custom infoType detectors for password patterns
    4. Cloud Audit Logs monitoring
    Show Answer

    Answer: b – Sensitive Data Protection can automatically discover unencrypted secrets in Cloud Functions and Cloud Run environment variables and report findings to Security Command Center.

  6. An enterprise using Security Command Center Enterprise wants to profile their Amazon S3 data for sensitive information. What prerequisite is required?
    1. A VPN connection between GCP and AWS
    2. An AWS connector in Security Command Center with Sensitive Data Protection permissions
    3. A hybrid job trigger configured in Sensitive Data Protection
    4. Amazon S3 data must be replicated to Cloud Storage first
    Show Answer

    Answer: b – Multicloud discovery for Amazon S3 (and Azure Blob Storage) requires Security Command Center Enterprise with the appropriate cloud connector configured.

  7. Which of the following are valid de-identification techniques in Sensitive Data Protection? (Choose 3)
    1. Masking
    2. Format-preserving encryption
    3. Dictionary replacement
    4. Data replication
    5. Column deletion
    Show Answer

    Answer: a, b, c – Masking, format-preserving encryption, and dictionary replacement are all supported de-identification techniques.

Reference

Google Cloud Security Services Cheat Sheet

Cloud Armor

  • Cloud Armor protects the applications from multiple types of threats, including distributed denial-of-service (DDoS) attacks and application attacks like cross-site scripting (XSS) and SQL injection (SQLi).
  • Cloud Armor is offered in two service tiers: Cloud Armor Standard and Cloud Armor Enterprise (previously Managed Protection Plus).
  • Cloud Armor Standard includes:
    • Pay-as-you-go pricing
    • Always-on L3/L4 volumetric and protocol-based DDoS protection
    • Global and regional security policies
    • Preconfigured WAF rules (OWASP Top 10)
  • Cloud Armor Enterprise includes all Standard features plus:
    • Adaptive Protection with full ML-based L7 DDoS detection and suggested rules
    • Advanced network DDoS protection for external network LBs, protocol forwarding, and VMs with public IPs
    • Threat intelligence integration
    • Bot management with reCAPTCHA Enterprise integration
    • DDoS bill protection
    • Named IP lists
  • Cloud Armor supports applications deployed on Google Cloud, in a hybrid deployment, or in a multi-cloud architecture.
  • Cloud Armor is implemented at the edge of Google’s network in Google’s points of presence (PoP).
  • Security policies protect applications running behind a load balancer from DDoS and other web-based attacks
  • Backend service can have only one security policy associated with it
  • Prioritized rules define configurable match conditions, actions (allow or deny) and order in a security policy
  • Cloud Armor provides Preview mode that helps evaluate and preview the rules before going live.
  • Hierarchical Security Policies (GA Oct 2025) facilitate centralized control at the organization and folder level, enabling policy delegation and consistent enforcement across projects.
  • Enhanced WAF inspection (GA Feb 2026) expands request body inspection from 8 KB to 64 KB for all preconfigured WAF rules, improving detection of sophisticated malicious content.
  • Adaptive Protection uses ML to automatically detect and suggest mitigations for L7 DDoS attacks. Full alerts (with suggested rules) require Cloud Armor Enterprise; Standard provides basic alerts only.
  • Rate Limiting supports throttle and ban actions based on per-client request rates.

Cloud Identity-Aware Proxy

  • Identity-Aware Proxy (IAP) allows managing access to HTTP-based apps both on Google Cloud and outside of Google Cloud.
  • IAP is based on the BeyondCorp zero-trust security model, establishing a zero-trust perimeter on the public internet for secure remote work without the need for a traditional VPN.
  • IAP intercepts web requests sent to the application, authenticates the user making the request using Google Identity Service, and only lets the requests through if they come from an authorized user. In addition, it can modify the request headers to include information about the authenticated user.
  • IAP helps establish a central authorization layer for applications accessed by HTTPS to use an application-level access control model instead of relying on network-level firewalls.
  • IAP uses Google identities and IAM and can leverage external identity providers as well like OAuth with Facebook, GitHub, Microsoft, SAML, etc.
  • IAP can be configured to use JSON Web Tokens (JWT) as signed headers to make sure that a request to the app is authorized and doesn’t bypass IAP.
  • IAP for Cloud Run (2025) enables IAP directly on Cloud Run services in a single click, with no load balancers required and at no added cost for IAP itself.
  • Google Cloud does not charge for IAP (with some exceptions for programmatic workloads at high scale).
  • Note: The IAP OAuth Admin API was deprecated in January 2025 and shut down in September 2025. Use the IAM API for IAP resource management.

Sensitive Data Protection (formerly Cloud DLP)

  • Cloud Data Loss Prevention (Cloud DLP) has been renamed to Sensitive Data Protection (SDP), now encompassing a broader family of services for discovering, classifying, and protecting sensitive data.
  • Sensitive Data Protection is a fully managed service designed to help discover, classify, and protect the most sensitive data.
  • provides two key features:
    • Classification is the process to inspect the data and know what data we have, how sensitive it is, and the likelihood.
    • De-identification is the process of removing, masking, replacing information from data.
  • uses information types – or infoTypes – to define what it scans like credit card numbers, email addresses, etc.
  • provides various built-in infoType detectors and supports custom ones
  • supports inspection rules to fine-tune scan results using:
    • Exclusion rules decrease the number of findings
    • Hotword rules increase the quantity or change the likelihood value of findings
  • provides likelihood, which indicates how likely it is that a piece of data matches a given infoType like VERY_LIKELY or POSSIBLE, etc.
  • supports Text Classification and Reduction
  • supports Image Classification and Reduction, where the image is handled using its base64 encoded version
  • supports storage classification with scans on data stored in Cloud Storage, BigQuery, Datastore, and Cloud SQL
  • supports scanning of binary, text, image, Microsoft Word, PDF, and Apache Avro files
  • supports Templates to decouple configuration from implementation and manage large-scale rollouts
  • Discovery Service (continuous monitoring) automatically discovers, classifies, and profiles data across the organization, folder, or project, identifying where sensitive and high-risk data reside.
  • Discovery for Vertex AI (2025) extends automated data discovery to Vertex AI datasets and tuning jobs, helping understand data sensitivity in AI training and fine-tuning data.
  • Conversational content inspection (2026) supports inspecting and de-identifying conversational content within ContentItem requests.
  • Sensitive Data Protection is deeply integrated with Security Command Center, feeding data risk insights into the SCC risk engine.
  • Note: Integration with Data Catalog was deprecated (Sept 2025) and discontinued (Jan 2026). SDP discovery results now flow to Security Command Center instead.

Security Command Center – SCC

  • is a Security and risk management platform for Google Cloud
  • helps generate curated insights that provide a unique view of incoming threats and attacks to the assets, which include organization, projects, instances, and applications
  • displays possible security risks, called findings, that are associated with each asset.
  • is available in three tiers: Standard, Premium, and Enterprise (Enterprise is deprecated, shutting down May 21, 2027)
  • SCC Standard Tier (Enhanced, April 2026) is now automatically enabled for eligible customers and includes:
    • AI Protection dashboard with detection of unprotected Gemini inference and guardrail violations
    • 44+ misconfiguration checks based on Google Cloud Security Essentials (GCSE) framework
    • Agentless critical vulnerability scanning
    • Graph-driven risk insights
    • Data Security Posture Management (DSPM) for Vertex AI, BigQuery, and Cloud Storage
    • Compliance Manager with automated monitoring against GCSE
  • SCC Premium Tier adds:
    • Full Security Health Analytics vulnerability scanning
    • Event Threat Detection
    • Container Threat Detection
    • Virtual Machine Threat Detection
    • Cloud Run Threat Detection
    • Attack path simulation and risk scoring
  • provides built-in services:
    • Security Health Analytics provides managed vulnerability assessment scanning that can automatically detect the highest severity vulnerabilities and misconfigurations across assets.
    • Web Security Scanner custom scans provide granular information about application vulnerability findings like outdated libraries, XSS, etc.
    • Sensitive Data Protection (formerly Cloud DLP) discovers, classifies, and protects sensitive data
    • Cloud Armor protects Google Cloud deployments against threats
    • Container Threat Detection can detect the most common container runtime attacks
    • Virtual Machine Threat Detection scans VMs to detect potentially malicious applications such as cryptocurrency mining software, kernel-mode rootkits, and malware
    • Cloud Run Threat Detection continuously monitors Cloud Run resources to detect common runtime attacks
    • Event Threat Detection monitors the organization’s Cloud Logging stream and consumes logs to detect Malware, Cryptomining, brute-force attacks, and identity-based attacks
    • Phishing Protection helps prevent users from accessing phishing sites by classifying malicious content that uses the brand and reporting the unsafe URLs to Google Safe Browsing
    • Continuous Exports, which automatically manage the export of new findings to Pub/Sub.
  • Note: The Anomaly Detection service has been replaced by Event Threat Detection and VM Threat Detection capabilities.
  • Note: Forseti Security (open-source toolkit) has been archived and is no longer maintained. Use SCC built-in services instead.
  • Note: SCC Enterprise tier was deprecated May 21, 2026 and will shut down May 21, 2027. Organizations will automatically move to Premium tier.

DDoS Protection and Mitigation

  • Distributed Denial of Service (DDoS) Protection and Mitigation is a shared responsibility between Google Cloud and the Customer
  • DDoS attack is an attempt to render the service or application unavailable to the end-users using multiple sources
  • DDoS Protection and Mitigation Best Practices
    • Reduce the Attack Surface
      • Isolate and secure network using VPC, subnets, firewall rules, tags and IAM
      • Google provides Anti-spoofing protection and Automatic isolation between virtual networks
    • Isolate Internal Traffic
      • Use private IPs and avoid using Public IPs
      • Use NAT Gateway and Bastion host
      • Use Internal Load Balancer for internal traffic
    • Enable Proxy-based Load Balancing
      • HTTP(S) or SSL proxy load balancer uses GFE that helps mitigate and absorb layer 4 and other attacks
      • Disperse traffic across multiple regions
    • Scale to Absorb the Attack
      • Use GFE for protection
      • Use Anycast-based load balancing to provide single anycast IP to FE
      • Use Autoscaling to scale backend services as per the demand
    • Protection using CDN Offloading
      • CDN acts as a proxy and can help render cache content reducing the load on the origin servers
    • Deploy Cloud Armor for WAF and DDoS Protection
      • Cloud Armor Enterprise provides advanced DDoS protection, adaptive protection, and DDoS bill protection
    • App Engine Deployment
      • A fully multi-tenant system with isolation
    • Google Cloud Storage
      • Use signed URLs to access Google Cloud Storage
    • API Rate Limiting
      • Define rate limiting based on the number of allowed requests
      • API Rate limits are per applied per-project basis
      • Cloud Armor supports rate limiting with throttle and ban actions
    • Resource Quotas
      • Quotas help prevent unforeseen spikes in usage

Access Context Manager

  • Access Context Manager allows organization administrators to define fine-grained, attribute-based access control for projects and resources
  • helps prevent data exfiltration
  • helps reduce the size of the privileged network and move to a model where endpoints do not carry ambient authority based on the network.
  • helps define desired rules and policy but isn’t responsible for policy enforcement. The policy is configured and enforced across various points, such as VPC Service Controls.
  • supports custom organization policies (GA 2025) for creating custom constraints beyond the built-in access levels.
  • supports individual VPC network members in a perimeter and ingress rules to authorize specific VPC networks to access a perimeter.
  • works with Chrome Enterprise Premium (formerly BeyondCorp Enterprise) for context-aware access that integrates device trust, user identity, and location into access decisions.

Model Armor (New – 2025)

  • Model Armor is a Google Cloud service designed to enhance the security and safety of AI applications that use Large Language Models (LLMs).
  • Works by proactively screening LLM prompts and responses, protecting against various risks and ensuring responsible AI practices.
  • Key capabilities:
    • Prompt injection detection — detects and blocks prompt injection and jailbreak attempts
    • Sensitive data protection — detects and prevents exposure of PII, financial info, and credentials in prompts and responses
    • Content safety — filters harmful, toxic, or inappropriate content
    • Policy enforcement — identifies and reports potential policy violations and can actively block actions
  • Protects all LLMs (including Gemini, OpenAI, Anthropic, Llama, and more) via a REST API.
  • Offers no-code in-line protection integrated with Google Cloud services including Gemini Enterprise Agent Platform.
  • Can integrate with Agent Gateway for securing agentic AI workflows (2026).
  • Integrated with Security Command Center for AI security posture visibility.

FIPS 140-2 / 140-3 Validated

  • The NIST developed the Federal Information Processing Standard (FIPS) Publication 140-2 (and its successor FIPS 140-3) as a security standard that sets forth requirements for cryptographic modules, including hardware, software, and/or firmware, for U.S. federal agencies.
  • FIPS 140-2/140-3 Validated certification was established to aid in the protection of digitally stored unclassified, yet sensitive, information.
  • Google Cloud Platform uses a FIPS 140-2 validated encryption module called BoringCrypto in its production environment.
  • Google’s BoringCrypto module has also received a FIPS 140-3 certificate (#4735), reflecting the transition to the newer standard.
  • Certifications issued under FIPS 140-2 remain valid and acceptable for federal compliance programs until their expiration date.
  • Data in transit to the customer and between data centers, and data at rest are encrypted using FIPS 140-2 validated encryption (or newer).
  • BoringCrypto module that achieved FIPS 140-2/140-3 validation is part of the BoringSSL library.
  • BoringSSL library as a whole is not FIPS validated
  • In order to operate using only FIPS-validated implementations:
    • Google’s Local SSD storage product is automatically encrypted with NIST approved ciphers, but Google’s current implementation for this product doesn’t have a FIPS validation certificate. If you require FIPS-validated encryption on Local SSD storage, you must provide your own encryption with a FIPS-validated cryptographic module.
    • Google automatically encrypts traffic between VMs that travels between Google data centers using NIST-approved encryption algorithms, but this implementation does not have a FIPS validation certificate. If you require this traffic to be encrypted with a FIPS-validated implementation, you must provide your own.
    • Clients connecting to Google infrastructure with TLS clients must be configured to require use of secure FIPS-compliant algorithms; if the TLS client and GCP’s TLS services agree on an encryption method that is incompatible with FIPS, a non-validated encryption implementation will be used.
    • Applications built and operated on GCP might include their own cryptographic implementations; in order for the data they process to be secured with a FIPS-validated cryptographic module, you must integrate such an implementation yourself.
  • All Google Cloud regions and zones currently support FIPS 140-2 validated encryption.
  • GKE supports FIPS 140-2 validated encryption using BoringCrypto, with nodes configured to use FIPS-compliant cryptographic modules.

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