BigID is a leading data intelligence platform designed to help organizations discover, classify, and protect sensitive data across on-premises, cloud, and hybrid environments. Leveraging advanced AI and machine learning, BigID enables businesses to improve data privacy, security, compliance, and governance. It provides automated data discovery, cataloging, and risk assessment to help organizations manage their data effectively while adhering to regulatory standards like GDPR, CCPA, and HIPAA.

What is BigID?

BigID is a modern data intelligence platform that enables enterprises to gain deep visibility into their structured and unstructured data. It helps organizations understand where their sensitive data is stored, how it’s being used, and who has access to it. By providing automated data discovery and classification, BigID helps businesses comply with data protection regulations and secure their critical information assets.

Key Characteristics of BigID:

• AI-Driven Data Discovery: Automatically scans and identifies sensitive data across various data sources.
• Comprehensive Data Classification: Categorizes data based on type, sensitivity, and regulatory requirements.
• Data Privacy and Compliance Management: Ensures adherence to GDPR, CCPA, HIPAA, and other regulations.
• Integration with Security and Privacy Tools: Works with SIEMs, DLP solutions, and cloud security platforms.
• Cloud-Native and Scalable: Supports multi-cloud and hybrid environments.

Top 10 Use Cases of BigID

1. Data Discovery and Classification
   • Automatically identifies, classifies, and catalogs sensitive data across all storage locations.
2. Regulatory Compliance (GDPR, CCPA, HIPAA, etc.)
   • Helps businesses meet global data privacy laws by enforcing compliance policies and generating audit reports.
3. Data Governance
   • Provides data lineage and ownership tracking to maintain proper governance and accountability.
4. Sensitive Data Protection
   • Identifies high-risk data and integrates with security tools to enforce protection policies.
5. Data Minimization and Retention Management
   • Helps organizations reduce data storage costs by managing data lifecycle policies.
6. Cloud Security and Data Access Controls
   • Secures sensitive data across AWS, Azure, Google Cloud, and SaaS applications.
7. Data Risk Assessment
   • Identifies potential data risks and provides actionable insights for remediation.
8. Third-Party Risk Management
   • Monitors and assesses the security of third-party data processors and vendors.
9. Data Subject Rights Automation (DSAR)
   • Automates response to data access, deletion, and correction requests from individuals under privacy laws.
10. Data Security and Incident Response
    • Helps organizations detect data breaches and respond to security incidents effectively.

Features of BigID

1. Automated Data Discovery – Uses AI and machine learning to scan and identify sensitive data across all repositories.
2. Data Classification and Tagging – Categorizes data based on sensitivity, type, and regulatory requirements.
3. Privacy and Compliance Management – Ensures regulatory compliance with built-in frameworks for GDPR, CCPA, HIPAA, etc.
4. Risk Analysis and Reporting – Identifies data security risks and provides detailed reports for analysis.
5. Data Cataloging – Organizes and indexes data for easy search, retrieval, and governance.
6. Data Subject Request (DSR) Automation – Streamlines responses to data access and deletion requests.
7. Cloud and SaaS Integration – Works seamlessly with AWS, Azure, Google Cloud, and third-party SaaS applications.
8. Data Security and Access Control – Monitors and enforces access policies to prevent unauthorized use.
9. Integration with SIEM and DLP – Enhances security by connecting with existing data protection and monitoring tools.
10. Data Retention and Minimization – Helps organizations clean up redundant, obsolete, and trivial (ROT) data.

How BigID Works and Architecture

1. Data Discovery and Scanning

• BigID scans and analyzes structured and unstructured data across databases, cloud storage, file systems, and applications.
• It uses machine learning and pattern recognition to identify sensitive data.

2. Data Classification and Tagging

• Data is automatically classified based on content, metadata, and context.
• Tags are assigned to sensitive information, helping organizations apply security policies.

3. Data Governance and Compliance Monitoring

• The platform continuously monitors data for compliance with privacy regulations.
• Custom rules can be created to track compliance adherence and generate audit-ready reports.

4. Risk Analysis and Remediation

• BigID provides a risk assessment dashboard that highlights security vulnerabilities and compliance gaps.
• Automated workflows help organizations mitigate risks through data protection measures.

5. Integration and Automation

• BigID integrates with existing security tools, SIEMs, and cloud security solutions to enhance data protection.
• APIs and connectors allow seamless automation of data privacy workflows.

How to Install BigID

BigID is a comprehensive data discovery and privacy management platform that helps organizations find, classify, and manage sensitive data across their environment. Installing BigID typically involves deploying the BigID Platform components, such as the BigID Data Discovery service and the BigID Console for managing and monitoring your data.

While BigID is mainly configured and managed via a web interface, you can automate its installation and configuration using command-line tools, scripts, or cloud automation tools like Terraform.

Here is a guide on how to install and configure BigID programmatically.

1. Prerequisites

Before starting the installation, ensure the following:

• A valid BigID license.
• Linux or Windows systems for installing BigID components.
• Docker installed (for containerized deployments).
• Sufficient disk space (usually 10 GB or more).
• BigID account for downloading installation files and API access.

2. Install BigID on Linux Using Docker

BigID is commonly deployed using Docker containers for flexibility and scalability. Here’s how to install BigID using Docker.

Step 1: Install Docker

First, ensure that Docker is installed on your system. If you don’t have Docker installed, you can do so by following the instructions for your system:

# For Ubuntu
sudo apt-get update
sudo apt-get install -y docker.io

# For RHEL/CentOS
sudo yum install -y docker

Step 2: Download BigID Docker Image

BigID provides a Docker image that can be used for installation. Pull the Docker image from the BigID Docker registry:

# Pull the BigID Docker image
docker pull bigid/bigid:latest

Step 3: Run BigID Containers

Once the Docker image is pulled, you can run the BigID Platform components using the following command:

# Start BigID Data Discovery and Console containers
docker run -d --name bigid_console -p 8080:8080 bigid/bigid:latest

This command starts the BigID Console on port 8080. You can access the console from a web browser at http://<your-server-ip>:8080.

You can also run other required containers, such as BigID Data Discovery or BigID Search, based on your needs. For example:

docker run -d --name bigid_data_discovery -p 8081:8081 bigid/bigid:latest

Step 4: Verify the Installation

To verify that the containers are running, use the following command:

docker ps

This will show all running containers, including BigID Console and BigID Data Discovery.

3. Install BigID on Windows Using Docker

For Windows systems, the installation process is similar, but you need to have Docker Desktop installed and running.

Step 1: Install Docker Desktop

Download and install Docker Desktop for Windows from the Docker website. After installation, ensure the Docker is running.

Step 2: Pull the BigID Docker Image

Just like with Linux, pull the BigID Docker image:

docker pull bigid/bigid:latest

Step 3: Run BigID Containers

Run the BigID Console and other components:

docker run -d --name bigid_console -p 8080:8080 bigid/bigid:latest

Step 4: Verify the Installation

Ensure that the containers are running correctly by using:

docker ps

4. Install BigID Using Kubernetes (Optional)

For cloud-native deployments or larger organizations, you may want to use Kubernetes to deploy BigID.

Step 1: Create a Kubernetes Cluster

Set up a Kubernetes cluster using a cloud service (e.g., Google Kubernetes Engine (GKE), Amazon EKS, or Azure AKS), or use a local tool like Minikube to simulate a Kubernetes cluster for testing purposes.

Step 2: Deploy BigID Using Kubernetes

You can use a Helm chart or Kubernetes manifest files to deploy BigID on a Kubernetes cluster.

# Example BigID deployment manifest (bigid-deployment.yaml)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: bigid
spec:
  replicas: 1
  selector:
    matchLabels:
      app: bigid
  template:
    metadata:
      labels:
        app: bigid
    spec:
      containers:
      - name: bigid
        image: bigid/bigid:latest
        ports:
        - containerPort: 8080

Step 3: Apply the Manifest to Deploy BigID

Run the following command to deploy BigID using Kubernetes:

kubectl apply -f bigid-deployment.yaml

This will deploy BigID on your Kubernetes cluster. You can expose the service using LoadBalancer or Ingress to make it accessible via a web browser.

5. Automate BigID Configuration with APIs

After installation, BigID provides REST APIs to automate various tasks such as retrieving findings, managing policies, and running discovery jobs. Here’s an example of how you can interact with BigID APIs programmatically.

Step 1: Obtain an API Token

To authenticate and interact with the BigID API, you will first need an API token. You can obtain the token by logging into the BigID Console and generating it in the API section.

Step 2: Make API Requests

Here’s an example of how to interact with the BigID API using Python to get a list of findings:

import requests

# BigID API endpoint
api_url = "http://<bigid-server-ip>:8080/api/v1/findings"

# BigID API authentication
headers = {
    'Authorization': 'Bearer YOUR_API_TOKEN',
    'Content-Type': 'application/json'
}

# Fetch findings
response = requests.get(api_url, headers=headers)

if response.status_code == 200:
    findings = response.json()
    print("BigID Findings:", findings)
else:
    print(f"Failed to retrieve findings: {response.status_code}")

Replace <bigid-server-ip> with the IP address or hostname of your BigID Console and YOUR_API_TOKEN with your actual API token.

6. Monitor and Maintain BigID

Once BigID is installed, you can:

• Monitor your data discovery processes through the BigID Console.
• Create and manage policies and jobs for data classification.
• Use REST APIs to interact with BigID programmatically.
• Configure alerts and notifications for sensitive data findings.

7. Automate with Terraform (Optional)

You can use Terraform to automate the provisioning of infrastructure for BigID in cloud environments. Below is an example configuration to deploy BigID on AWS using Terraform.

provider "aws" {
  region = "us-east-1"
}

resource "aws_instance" "bigid_instance" {
  ami = "ami-0abcdef1234567890" # Example AMI ID for Ubuntu
  instance_type = "t2.medium"
  key_name = "my-key"
  tags = {
    Name = "BigID-Instance"
  }
}

resource "aws_security_group" "bigid_sg" {
  name        = "bigid_sg"
  description = "Allow inbound traffic for BigID"
  ingress {
    from_port   = 8080
    to_port     = 8080
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

Run the following Terraform commands to deploy the infrastructure:

terraform init
terraform plan
terraform apply

Basic Tutorials of BigID: Getting Started

Step 1: Log in to the BigID Dashboard

• Access the BigID console using your admin credentials.

Step 2: Add Data Sources

1. Navigate to Data Sources.
2. Select a new data source (e.g., AWS S3, SQL Server, Google Drive).
3. Authenticate and configure access permissions.

Step 3: Start a Data Scan

• Click on Scan Now to start analyzing data across connected sources.

Step 4: Review Data Classification Results

• Go to Insights > Data Classification to view categorized data.

Step 5: Set Up Compliance Rules

• Enable compliance frameworks like GDPR and CCPA under the Compliance Center.

Step 6: Automate Risk Remediation

• Configure automated risk response actions under Risk Management.

The post What is BigID and Use Cases of BigID? appeared first on Artificial Intelligence.

After several high-profile cases, it’s understandable that governments would want to start regulating artificial intelligence (AI), and biometric technology in particular. The Clearview AI scandal has shown that people are really ‘not OK’ with the knowledge that companies scraped the internet for private images in order to train a facial recognition AI solution they then turned around and sold to law enforcement agencies.

Additionally, a number of cases by civil rights groups have shown that when AI is employed to make decisions about providing credit, rendering a verdict, or simply verifying the identity of a person, minorities are often discriminated against.

At the end of April, the EU adopted a proposal for a regulation called the Artificial Intelligence Act (AIA) designed to regulate AI-based solutions. When these new rules go fully into effect, the EU hopes to become a global trendsetter in AI regulation. The framework of these new laws are similar to the General Data Privacy Regulation (GDPR), which went live in 2018: The legal machinations trigger whenever personal data of an EU citizen is processed anywhere in the world.

New legislation on the horizon

The good news for AI and biometrics companies is that GDPR took two years to move from the proposal stage to the regulation finally adopted by the bloc, so the business world had time to prepare. In its current form, the AIA looks similar to GDPR in what it seeks to accomplish: a means to give end-users a way to control the collection and use of their personal data and digital likeness. In a word: transparency.

AIA argues that the end-user should know, at all times, that they are being judged by AI-powered technology. Is that a chatbot or a live person helping them online? Is their likeness being collected for biometric identification?

Companies that already offer settings to disallow collection of biometric data, or can integrate well with personal data management systems, will find they have the advantage under this emergent new regulatory scrutiny. For biometrics companies in general, the adherence to the final version of these new rules will be required for the correct collection, filtering, and labeling of datasets.

A spate of independent U.S. regulations adds to the complexity

The fragmented nature of the U.S. rules governing the collecting of biometric data has already cost Facebook upward of half a billion dollars, with similar lawsuits against Google, Amazon and Microsoft underway. The absence of clear rules at a federal level leaves it up to the states to decide what AI companies are allowed to collect in terms of personal information without their user’s consent. However, California’s CCPA, Illinois’ PIPA, Massachusetts Data Privacy Act, New York Privacy Act, and the Hawaii Consumer Privacy Protection Act all have the same aim.

For instance, New York’s strict privacy statute has a private right of action for any violation of the law, applicable to all businesses. This means virtually anyone who feels they deserve to pursue legal recourse against a New York business they feel might have violated their rights as described under the state’s privacy statute can do so by simply going down to the civil courthouse, and filing a lawsuit.

Regulation yields… growth?

From an industry standpoint, a common set of regulations governing the use of AI would be a great way to reduce friction when introducing biometrics-based solutions to different markets across large markets like the EU constituent countries and fifty U.S. states. Under one framework, companies can focus on creating solutions that offer the maximum amount of privacy and transparency while solving the kinds of problems that AI exists to solve in the first place.

What we can expect to see in the near future is a flourishing of companies that will provide third party certifications of compliance with the new regulation—from dataset audit to algorithm bias measurements. Some of these services are already standardized via the U.S.-based National Institute of Standards and Technologies (NIST) that, for instance, compares accuracy and speed of facial recognition and fingerprint algorithms, among others. NIST even conducted an extremely thorough comparison of all submitted algorithms regarding their bias against minority groups or ability to recognize faces when wearing protective masks.

Universal regulation also singles out large-scale, facial-recognition-driven surveillance of open spaces as an especially high-risk application of the technology. Due to its “big brother” nature, it is understandable that such an application will be a domain of only a handful of companies, with the rest shying away from such controversy.

There is a growing number of benign applications of biometrics that improve the life of the users without opening their personal data to possible misuse, and that is where the future lies. The COVID-19 pandemic has shown that biometric applications allowed certain industries like financial services and telcos to continue doing business that used to be conducted in-person to confirm proof of identity (opening a bank account, for example) even during the lockdowns. In fact, the technology has proven to be so convenient that even branches adopted digital onboarding instead of their former paper processes. This is where the strength of the technology lies, solving problems in a way that facilitates and maximizes convenience.

Final thoughts

AI is only as intelligent as the data we feed it. If you show a machine learning algorithm 100,000 pictures of a fish, it eventually can draw conclusions about the fish, but a toddler can see one or two pictures of a fish and determine whether or not the following picture is a fish or something else.

However, researchers aren’t always entirely sure as to how AI comes to the decisions that it makes, other than if you feed it biased information, you get biased results. This is why facial recognition has problems with correctly identifying people with darker skin. Datasets of photos used to train facial recognition algorithms contain more images of people with lighter skin than darker skinned people. Resultantly, there’s been an industry push towards explainable AI. ‘XAI’ so you can see what decisions the machine went through to come to its verdict.

At Innovatrics, we found that our AI algorithm is able to identify faces behind face masks, even though it has not been taught to do so. It’s virtually incomprehensible how AI arrives at its decisions because up until now, transparency or explainability haven’t been major outcomes AI engineers have considered when looking for results.

Looking towards the future, as new regulations governing the technology come into effect, explainability and comprehensibility will become the standard. Companies who value transparency and their customer’s privacy will come out ahead in this new era of machine learning.

The post Will the proposed EU AI rules become the GDPR for biometrics? appeared first on Artificial Intelligence.

A German privacy watchdog has ordered Google  to cease manual reviews of audio snippets generated by its voice AI. 

This follows a leak last month of scores of audio snippets from the Google Assistant service. A contractor working as a Dutch language reviewer handed more than 1,000 recordings to the Belgian news site VRT which was then able to identify some of the people in the clips. It reported being able to hear people’s addresses, discussion of medical conditions, and recordings of a woman in distress.

The Hamburg data protection authority told Google of its intention to use Article 66 powers of the General Data Protection Regulation (GDPR) to begin an “urgency procedure” under Article 66 of GDPR last month.

Article 66 allows a DPA to order data processing to stop if it believes there is “an urgent need to act in order to protect the rights and freedoms of data subjects”.

This appears to be the first use of the power since GDPR came into force across the bloc in May last year.

Google says it responded to the DPA on July 26 to say it had already ceased the practice — taking the decision to manually suspend audio reviews of Google Assistant across the whole of Europe, and doing so on July 10, after learning of the data leak.

Last month it also informed its lead privacy regulator in Europe, the Irish Data Protection Commission (DPC), of the breach — which also told us it is now “examining” the issue that’s been highlighted by Hamburg’s order.

The Irish DPC’s head of communications, Graham Doyle, said Google Ireland filed an Article 33 breach notification for the Google Assistant data “a couple of weeks ago”, adding: “We note that as of 10 July Google Ireland ceased the processing in question and that they have committed to the continued suspension of processing for a period of at least three months starting today (1 August). In the meantime we are currently examining the matter.”

It’s not clear whether Google will be able to reinstate manual reviews in Europe in a way that’s compliant with the bloc’s privacy rules. The Hamburg DPA writes in a statement [in German] on its website that it has “significant doubts” about whether Google Assistant complies with EU data-protection law.

“We are in touch with the Hamburg data protection authority and are assessing how we conduct audio reviews and help our users understand how data is used,” Google’s spokesperson also told us.

In a blog post published last month after the leak, Google product manager for search, David Monsees, claimed manual reviews of Google Assistant queries are “a critical part of the process of building speech technology”, couching them as “necessary” to creating such products.

“These reviews help make voice recognition systems more inclusive of different accents and dialects across languages. We don’t associate audio clips with user accounts during the review process, and only perform reviews for around 0.2% of all clips,” Google’s spokesperson added now.

But it’s far from clear whether human review of audio recordings captured by any of the myriad always-on voice AI products and services now on the market will be able to be compatible with European’s fundamental privacy rights.

These AIs typically have trigger words for activating the recording function which streams audio data to the cloud. But the technology can easily be accidentally triggered — and leaks have shown they are able to hoover up sensitive and intimate personal data not just of their owner but anyone in their vicinity (which of course includes people who never got within sniffing distance of any T&Cs).

In its website the Hamburg DPA says the intended proceedings against Google are intended to protect the privacy rights of affected users in the immediate term, noting that GDPR allows for concerned authorities in EU Member States to issue orders of up to three months.

In a statement Johannes Caspar, the Hamburg commissioner for data protection, added: “The use of language assistance systems in the EU must comply with the data protection requirements of the GDPR. In the case of the Google Assistant, there are currently significant doubts. The use of language assistance systems must be done in a transparent way, so that an informed consent of the users is possible. In particular, this involves providing sufficient information and transparently informing those concerned about the processing of voice commands, but also about the frequency and risks of mal-activation. Finally, due regard must be given to the need to protect third parties affected by the recordings. First of all, further questions about the functioning of the speech analysis system have to be clarified. The data protection authorities will then have to decide on definitive measures that are necessary for a privacy-compliant operation. ”

The DPA also urges other regional privacy watchdogs to prioritize checks on other providers of language assistance systems — and “implement appropriate measures” — name-checking rival providers of voice AIs, Apple  and Amazon .

This suggests there could be wider ramifications for other tech giants operating voice AIs in Europe flowing from this single notification of an Article 66 order.

The real enforcement punch packed by GDPR is not the headline-grabbing fines, which can scale as high as 4% of a company’s global annual turnover — it’s the power that Europe’s DPAs now have in their regulatory toolbox to order that data stops flowing.

“This is just the beginning,” one expert on European data protection legislation told us, speaking on condition of anonymity. “The Article 66 chest is open and it has a lot on offer.”

In a sign of the potential scale of the looming privacy problems for voice AIs, Apple also said earlier today that it’s suspending a similar human review ‘quality control program’ for its Siri voice assistant.

The move, which does not appear to be linked to any regulatory order, follows a Guardian report last week detailing claims by a whistleblower that contractors working for Apple ‘regularly hear confidential details’ on Siri recordings, such as audio of people having sex and identifiable financial details, regardless of the processes Apple uses to anonymize the records.

Apple’s suspension of manual reviews of Siri snippets applies worldwide.

The post Google ordered to halt human review of voice AI recordings over privacy risks appeared first on Artificial Intelligence.