Search results for AI data protection | Breaking Cybersecurity News | The Hacker News

Data is the lifeblood of productivity, and protecting sensitive data is more critical than ever. With cyber threats evolving rapidly and data privacy regulations tightening, organizations must stay vigilant and proactive to safeguard their most valuable assets. But how do you build an effective data protection framework? In this article, we'll explore data protection best practices from meeting compliance requirements to streamlining day-to-day operations. Whether you're securing a small business or a large enterprise, these top strategies will help you build a strong defense against breaches and keep your sensitive data safe. 1. Define your data goals When tackling any data protection project, the first step is always to understand the outcome you want. First, understand what data you need to protect. Identify your crown jewel data, and where you THINK it lives. (It's probably more distributed than you expect, but this is a key step to help you define your protection ...

The Irish Data Protection Commission (DPC) has announced that it has commenced a "Cross-Border statutory inquiry" into Google's foundational artificial intelligence (AI) model to determine whether the tech giant has adhered to data protection regulations in the region when processing the personal data of European users. "The statutory inquiry concerns the question of whether Google has complied with any obligations that it may have had to undertake an assessment, pursuant to Article 35[2] of the General Data Protection Regulation (Data Protection Impact Assessment), prior to engaging in the processing of the personal data of E.U./E.E.A. data subjects associated with the development of its foundational AI model, Pathways Language Model 2 (PaLM 2)," the DPC said . PaLM 2 is Google's state-of-the-art language model with improved multilingual, reasoning, and coding capabilities. It was unveiled by the company in May 2023. With Google's European headqu...

Austrian privacy non-profit noyb (none of your business) has sent Meta's Irish headquarters a cease-and-desist letter, threatening the company with a class action lawsuit if it proceeds with its plans to train users' data for training its artificial intelligence (AI) models without an explicit opt-in. The move comes weeks after the social media behemoth announced its plans to train its AI models using public data shared by adults across Facebook and Instagram in the European Union (E.U.) starting May 27, 2025, after it paused the efforts in June 2024 following concerns raised by Irish data protection authorities. "Instead of asking consumers for opt-in consent, Meta relies on an alleged 'legitimate interest' to just suck up all user data," noyb said in a statement. "Meta may face massive legal risks – just because it relies on an 'opt-out' instead of an 'opt-in' system for AI training." The advocacy group further noted that Met...

While some SaaS threats are clear and visible, others are hidden in plain sight, both posing significant risks to your organization. Wing's research indicates that an astounding 99.7% of organizations utilize applications embedded with AI functionalities. These AI-driven tools are indispensable, providing seamless experiences from collaboration and communication to work management and decision-making. However, beneath these conveniences lies a largely unrecognized risk: the potential for AI capabilities in these SaaS tools to compromise sensitive business data and intellectual property (IP). Wing's recent findings reveal a surprising statistic: 70% of the top 10 most commonly used AI applications may use your data for training their models. This practice can go beyond mere data learning and storage. It can involve retraining on your data, having human reviewers analyze it, and even sharing it with third parties. Often, these threats are buried deep in the fine print of Term...

AI-assisted coding and AI app generation platforms have created an unprecedented surge in software development. Companies are now facing rapid growth in both the number of applications and the pace of change within those applications. Security and privacy teams are under significant pressure as the surface area they must cover is expanding quickly while their staffing levels remain largely unchanged. Existing data security and privacy solutions are too reactive for this new era. Many begin with data already collected in production, which is often too late. These solutions frequently miss hidden data flows to third party and AI integrations, and for the data sinks they do cover, they help detect risks but do not prevent them. The question is whether many of these issues can instead be prevented early. The answer is yes. Prevention is possible by embedding detection and governance controls directly into development. HoundDog.ai provides a privacy code scanner built for exactly this p...

Generative AI is not arriving with a bang, it's slowly creeping into the software that companies already use on a daily basis. Whether it is video conferencing or CRM, vendors are scrambling to integrate AI copilots and assistants into their SaaS applications. Slack can now provide AI summaries of chat threads, Zoom can provide meeting summaries, and office suites such as Microsoft 365 contain AI assistance in writing and analysis. This trend of AI usage implies that the majority of businesses are awakening to a new reality: AI capabilities have spread across their SaaS stack overnight, with no centralized control. A recent survey found 95% of U.S. companies are now using generative AI, up massively in just one year. Yet this unprecedented usage comes tempered by growing anxiety. Business leaders have begun to worry about where all this unseen AI activity might lead. Data security and privacy have quickly emerged as top concerns, with many fearing that sensitive information could le...

In the era of rapidly advancing artificial intelligence (AI) and cloud technologies, organizations are increasingly implementing security measures to protect sensitive data and ensure regulatory compliance. Among these measures, AI-SPM (AI Security Posture Management) solutions have gained traction to secure AI pipelines, sensitive data assets, and the overall AI ecosystem. These solutions help organizations identify risks, control security policies, and protect data and algorithms critical to their operations.  However, not all AI-SPM tools are created equal. When evaluating potential solutions, organizations often struggle to pinpoint which questions to ask to make an informed decision. To help you navigate this complex space, here are five critical questions every organization should ask when selecting an AI-SPM solution: 1: Does the solution offer comprehensive visibility and control over AI and associated data risk? With the proliferation of AI models across enterprises, m...

Facebook, the social network platform owned by Meta, is asking for users to upload pictures from their phones to suggest collages, recaps, and other ideas using artificial intelligence (AI), including those that have not been directly uploaded to the service. According to TechCrunch, which first reported the feature, users are being served a new pop-up message asking for permission to "allow cloud processing" when they are attempting to create a new Story on Facebook. "To create ideas for you, we'll select media from your camera roll and upload it to our cloud on an ongoing basis, based on info like time, location or themes," the company notes in the pop-up. "Only you can see suggestions. Your media won't be used for ads targeting. We'll check it for safety and integrity purposes." Should users consent to their photos being processed on the cloud, Meta also states that they are agreeing to its AI terms , which allow it to analyze their med...

Employees are experimenting with AI at record speed. They are drafting emails, analyzing data, and transforming the workplace. The problem is not the pace of AI adoption, but the lack of control and safeguards in place. For CISOs and security leaders like you, the challenge is clear: you don't want to slow AI adoption down, but you must make it safe. A policy sent company-wide will not cut it. What's needed are practical principles and technological capabilities that create an innovative environment without an open door for a breach. Here are the five rules you cannot afford to ignore. Rule #1: AI Visibility and Discovery The oldest security truth still applies: you cannot protect what you cannot see. Shadow IT was a headache on its own, but shadow AI is even slipperier. It is not just ChatGPT, it's also the embedded AI features that exist in many SaaS apps and any new AI agents that your employees might be creating. The golden rule: turn on the lights. You need real-time visibi...

In the short time since their inception, ChatGPT and other generative AI platforms have rightfully gained the reputation of ultimate productivity boosters. However, the very same technology that enables rapid production of high-quality text on demand, can at the same time expose sensitive corporate data. A recent  incident , in which Samsung software engineers pasted proprietary code into ChatGPT, clearly demonstrates that this tool can easily become a potential data leakage channel. This vulnerability introduces a demanding challenge for security stakeholders, since none of the existing data protection tools can ensure no sensitive data is exposed to ChatGPT. In this article we'll explore this security challenge in detail and show how browser security solutions can provide a solution. All while enabling organizations to fully realize ChatGPT's productivity potential and without having to compromise on data security.  The ChatGPT data protection blind spot: How can you gover...

Microsoft on Tuesday unveiled the expansion of its Sentinel Security Incidents and Event Management solution (SIEM) as a unified agentic platform with the general availability of the Sentinel data lake. In addition, the tech giant said it's also releasing a public preview of Sentinel Graph and Sentinel Model Context Protocol ( MCP ) server to turn telemetry into a security graph and allow AI agents access an organization's security context in a standardized manner. "With graph-based context, semantic access, and agentic orchestration, Sentinel gives defenders a single platform to ingest signals, correlate across domains, and empower AI agents built in Security Copilot, VS Code using GitHub Copilot, or other developer platforms," Vasu Jakkal, corporate vice president at Microsoft Security, said in a post shared with The Hacker News. Microsoft released Sentinel data lake in public preview earlier this July as a purpose-built, cloud-native tool to ingest, manage...

Artificial intelligence (AI) holds immense potential for optimizing internal processes within businesses. However, it also comes with legitimate concerns regarding unauthorized use, including data loss risks and legal consequences. In this article, we will explore the risks associated with AI implementation and discuss measures to minimize damages. Additionally, we will examine regulatory initiatives by countries and ethical frameworks adopted by companies to regulate AI. Security risks  AI phishing attacks Cybercriminals can leverage AI in various ways to enhance their phishing attacks and increase their chances of success. Here are some ways AI can be exploited for phishing: -  Automated Phishing Campaigns:  AI-powered tools can automate the creation and dissemination of phishing emails on a large scale. These tools can generate convincing email content, craft personalized messages, and mimic the writing style of a specific individual, making phishing attempts app...

Cyber threat intelligence is an effective weapon in the ongoing battle to protect digital assets and infrastructure - especially when combined with AI. But AI is only as good as the data feeding it. Access to unique, underground sources is key. Threat Intelligence offers tremendous value to people and companies. At the same time, its ability to address organizations' cybersecurity needs and the benefits it offers vary by company, industry, and other factors. A common challenge with cyber threat intelligence (CTI) is that the data it produces can be vast and overwhelming, creating confusion and inefficiencies among security teams' threat exposure management efforts. Additionally, organizations have different levels of security maturity, which can make access to and understanding of CTI data difficult. Enter generative AI. Many cybersecurity companies – and more specifically, threat intelligence companies – are bringing generative AI to market to simplify threat intelligence a...