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ATLANTA, Jan. 20, 2026, CyberNewswire — Airlock Digital, a leader in proactive application control and endpoint security, announced the release of The Total Economic Impact (TEI) of Airlock Digital, an independent study commissioned by Airlock Digital and conducted … (more…)

The post News alert: Forrester study finds Airlock Digital’s app control cuts breaches to zero with 224% ROI first appeared on The Last Watchdog.

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Check Point dug into the details of VoidLink and found a sophisticated and quickly developed malware that was mostly generated using AI and putting a spotlight on what the future of cyber threats looks like.

The post VoidLink Represents the Future of AI-Developed Malware: Check Point appeared first on Security Boulevard.

Atlanta, GA, United States, 20th January 2026, CyberNewsWire

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Session 9C: Phishing & Fraud 2

Authors, Creators & Presenters: Boladji Vinny Adjibi (Georgia Tech), Athanasios Avgetidis (Georgia Tech), Manos Antonakakis (Georgia Tech), Michael Bailey (Georgia Tech), Fabian Monrose (Georgia Tech)

PAPER
The Guardians of Name Street: Studying the Defensive Registration Practices of the Fortune 500

Using orthographic, phonetic, and semantic models, we study the prevalence of defensive registrations related to a wide spectrum of transformations of the base domain names of Fortune 500 companies. As part of a large-scale evaluation, we explore several questions aimed at (a) understanding whether there are explainable factors (e.g., the size of the company's security team or its domain name's popularity rank) that correlate with a company's level of engagement regarding defensive registrations; (b) identifying the main actors in the defensive registration ecosystem that Fortune 500 companies rely upon; (c) uncovering the strategies used by these actors, and d) assessing the efficacy of those strategies from the perspective of queries emanating from a large Internet Service Provider (ISP). Overall, we identified 19,523 domain names defensively registered by 447 Fortune 500 companies. These companies engage in defensive registrations sparingly, with almost 200 companies having fewer than ten defensive registrations. By analyzing the registrations, we found many similarities between the types of domain names the companies registered. For instance, they all registered many TLD-squatting domain names. As it turns out, those similarities are due to the companies' reliance on online brand protection (OBP) service providers to protect their brands. Our analysis of the efficacy of the strategies of those OBPs showed that they register domain names that receive most of the potential squatting traffic. Using regression models, we learned from those strategies to provide recommendations for future defensive registrants. Our measurement also revealed many domain names that received high proportions of traffic over long periods of time and could be registered for only 15 USD. To prevent the abusive use of such domain names, we recommend that OBP providers proactively leverage passive DNS data to identify and preemptively register highly queried available domain names.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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In Star Trek, the Kobayashi Maru simulation is an unwinnable test faced by Starfleet cadet captains. The only way to “win” is to accept that you can’t. It’s a test of character — forcing cadet captains to choose between impossible options and live with the consequences. In many ways, our roles as cybersecurity leaders is the..

The post The Zero Risk Trap: How to Ditch Perfection and Prioritize Real Cyber Resilience appeared first on Security Boulevard.

Today’s data centers are hardened facilities with layered access controls, surveillance, redundancy and security teams focused on keeping threats out. Yet, even the most secure environment can be compromised by a single moment of trust, such as a legitimate-looking email that prompts someone to click a link. That’s the modern cybersecurity paradox. The perimeter can..

The post The Data Center Is Secure, But Your Users Are Not appeared first on Security Boulevard.

The numbers tell a stark story: $1.42 billion lost across 149 documented incidents in 2024 due to smart contract vulnerabilities, with access control flaws accounting for $953.2 million in damages alone. While the Web3 community debates the perfect AI solution for smart contract security, billions continue to drain from protocols that could have been protected..

The post Why Smart Contract Security Can’t Wait for “Better” AI Models appeared first on Security Boulevard.

There is a rise in cybersecurity threats in today’s rapidly changing digital landscape. Organizations have struggled to safeguard sensitive data and systems from ransomware and breaches. In fact, about 87% of security professionals report that AI-based cyberattacks are plaguing organizations worldwide. Traditional cybersecurity solutions are effective to a degree. However, they tend to be limited..

The post Cybersecurity in the Age of AIOps: Proactive Defense Strategies for IT Leaders appeared first on Security Boulevard.

Why Stitched Together Platforms Quietly Increase Breach Probability In today’s cybersecurity market, nearly every vendor claims to offer an integrated or unified platform. For buyers under pressure to reduce complexity, these promises are appealing. But beneath the marketing language lies a reality that many organizations only discover after a breach: integration does not equal unification.

The post The Hidden Cybersecurity Risk of “Integrated” Security Platforms appeared first on Seceon Inc.

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The privacy rights group Fight for the Future was one of 44 organizations that sent a letter to lawmakers urging them to pull back on funding for ICE, noting the growing threats to U.S. citizens and others as the agency spends millions of dollars on its growing surveillance capabilities.

The post Fight for the Future, EFF, Others Push Back Against Growing ICE Surveillance appeared first on Security Boulevard.

The rapid adoption of artificial intelligence has forced organizations to confront a hard truth: AI changes the cybersecurity equation.

New attack surfaces, new misuse patterns and new forms of automation require a different approach to managing risk.

That’s why NIST has stepped forward.

Through its draft AI cybersecurity profile, NIST CSF 2.0 and the AI Risk Management Framework, NIST makes one thing clear: AI security must be grounded in proven cybersecurity principles, adapted for an AI-driven world. That’s where a focus on data trust comes in.

NIST provides an effective structure that can be a helpful guide for teams. In practice, building data trust is one of the most effective steps teams can take to enable safe, effective AI usage.

What is NIST’s view of AI security?

NIST does not treat AI security as a standalone discipline. Instead, it extends existing cybersecurity frameworks to account for how AI systems consume data, make decisions and act autonomously.

Across both NIST CSF 2.0 and the AI Risk Management Framework, several themes are consistent:

• Organizations must govern AI use intentionally
• Data and system dependencies must be understood before deployment
• Risk must be measured continuously, not assumed
• Controls must adapt as behavior changes

At the center of all of these themes is a growing problem: organizations lack confidence in how their data is accessed and used. Without that confidence, they cannot meaningfully govern AI risk, because they don’t know whether data is being used safely, appropriately or at all as intended.

What is data trust?

Data trust is the degree of confidence an organization has that its systems use data safely and appropriately.

This aligns naturally with NIST’s intent. It’s not about perfection. It’s about having enough clarity and control to be confident that data use matches policy, regulatory obligations and business intent.

In an AI-driven environment, this matters because systems can move quickly and at scale. When data is overexposed or misunderstood, AI can spread that risk faster than most teams can react.

How NIST frameworks use data trust to secure AI systems

NIST CSF 2.0 establishes the operational backbone for data trust.

• Govern defines expectations for how data and AI systems should be used
• Identify creates visibility into sensitive data and data flows
• Protect enforces appropriate access and safeguards
• Detect validates that data is being used as intended
• Respond and Recover preserve confidence when incidents occur

The AI Risk Management Framework builds on this foundation by focusing on AI-specific risk.

• Govern aligns AI use with organizational values
• Map documents data inputs and dependencies
• Measure evaluates whether AI systems behave in trustworthy ways
• Manage adapts controls as risk changes

Taken together, these frameworks describe the path to data trust, even if they don’t always use the term explicitly.

What does data trust mean in the AI era?

Traditionally, data security focused on protecting data at rest or in transit. AI changes the model because data is now actively used and manipulated by humans, applications and other AI systems across cloud platforms, SaaS tools, endpoints and GenAI services.

In this context, a practical definition of data trust is straightforward: you can explain, with evidence, that AI systems are accessing and using data safely and appropriately.

That typically means:

• Sensitive data is identified before it enters AI workflows
• Access reflects least privilege, not convenience
• Usage aligns with organizational policy and compliance obligations
• Risk is monitored continuously, not discovered after the fact

Without this foundation, AI introduces uncertainty instead of value.

Why NIST-aligned data trust matters for AI security

AI doesn’t create new data security problems. It magnifies existing ones.

If organizations lack visibility into where sensitive data lives, AI will find it anyway. If access controls are overly permissive, AI will inherit those permissions. If teams rely on static rules, AI-driven workflows will outpace them.

NIST explicitly warns against treating AI security as an overlay bolted onto existing programs, a theme reinforced across its AI RMF guidance and broader cybersecurity publications. Instead, AI risk must be integrated into core cybersecurity practices. A focus on data trust is what makes that integration tangible.

When teams can demonstrate that data is used safely and appropriately, AI becomes easier to govern and safer to scale.

How organizations build data trust using NIST guidance

Data trust isn’t achieved through policy alone. It’s built by applying NIST principles to how data is actually used, then validating that those controls work over time.

1. Continuous data visibility: NIST emphasizes understanding assets and dependencies. For AI, that starts with continuous discovery and classification of sensitive data across SaaS, cloud, endpoints and GenAI tools. Visibility cannot be periodic. AI usage evolves too quickly.
2. Context-driven risk evaluation: NIST calls for improved signal quality and risk measurement. Context provides that signal. Understanding who is accessing data, what they are doing and whether behavior aligns with normal patterns reduces noise and surfaces real risk.
3. Data-centric enforcement: NIST frameworks assume controls follow risk. In AI environments, risk follows the data. Enforcing policy based on data sensitivity rather than application boundaries enables safe AI adoption without adding friction.
4. Responsible use of AI for security: NIST also highlights the defensive potential of AI. With trusted data and strong context, AI can help prioritize risk, detect anomalies faster and reduce manual remediation. Used this way, AI strengthens security instead of undermining it.
5. Continuous verification of appropriate data use: NIST frameworks emphasize that trust must be continuously validated, not assumed. In practice, this means organizations must regularly verify that data is being accessed and used in ways that remain safe, appropriate and aligned with policy as AI systems, users and workflows evolve.

The impact on data security and the business

Organizations that apply NIST guidance with a data trust focus often see benefits that extend beyond AI initiatives.

Security teams gain better visibility into real risk, fewer false positives and faster response times. The business gains safer AI adoption, reduced risk of data leakage and greater confidence in AI-driven outcomes.

Most importantly, security evolves from a reactive compliance function into an enabler of innovation.

Why NIST and data trust matter now

AI adoption is accelerating whether organizations are ready or not. Employees are using AI tools. Adversaries are exploiting automation. Regulators are paying close attention.

NIST provides the framework for navigating this shift. A deliberate focus on data trust is a practical way to put that framework into action.

If AI is going to deliver real value, organizations need confidence that their systems use data safely and appropriately. That confidence is built through governance, visibility and continuous verification.

In the AI era, NIST shows the way. A disciplined approach to data trust is one of the clearest paths to follow it.

The post NIST’s Blueprint for AI Security: How Data Trust Enables AI Success appeared first on Security Boulevard.

Researchers with security firm Miggo used an indirect prompt injection technique to manipulate Google's Gemini AI assistant to access and leak private data in Google Calendar events, highlighting the challenges AI presents that traditional security measures can't address.

The post Exploiting Google Gemini to Abuse Calendar Invites Illustrates AI Threats appeared first on Security Boulevard.

 

The post When Security Incidents Break: The Questions Every CISO Asks (And How We Securely Built a Solution in Record Time) appeared first on Security Boulevard.

Eighteen months ago, it was plausible that artificial intelligence might take a different path than social media. Back then, AI’s development hadn’t consolidated under a small number of big tech firms. Nor had it capitalized on consumer attention, surveilling users and delivering ads.

Unfortunately, the AI industry is now taking a page from the social media playbook and has set its sights on monetizing consumer attention. When OpenAI launched its ChatGPT Search feature in late 2024 and its browser, ChatGPT Atlas, in October 2025, it kicked off a ...

The post Could ChatGPT Convince You to Buy Something? appeared first on Security Boulevard.

Alisa Viejo, United States, 20th January 2026, CyberNewsWire

The post One Identity Unveils Major Upgrade to Identity Manager, Strengthening Enterprise Identity Security appeared first on Security Boulevard.

Digitally signing Java applications improves authenticity, integrity, and trust. DigiCert KeyLocker allows you to sign .jar files securely using keys stored in DigiCert’s cloud-based Hardware Security Modules (HSMs) and the DigiCert KSP Library. This guide explains how to establish your environment and use JarSigner to sign Java applications from KeyLocker. What Is DigiCert KeyLocker? DigiCert… Read More How to Configure KeyLocker for JarSigner using the DigiCert KSP Library?

The post How to Configure KeyLocker for JarSigner using the DigiCert KSP Library? appeared first on SignMyCode - Resources.

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Kimwolf botnet exploits smart gadgets for DDoS attacks, highlighting security lapses in device protection and supply chains.

The post What’s On the Tube Or Rather in the Tube: Kimwolf Targets Android-based TVs and Streaming Devices  appeared first on Security Boulevard.

In late 2025 and early 2026, one of the world’s most advanced scientific organizations, the European Space Agency (ESA), faced a string of cyberattacks that exposed severe weaknesses in its cybersecurity posture. Hackers stole hundreds of gigabytes of data. Among the data stolen were proprietary software, credentials, and mission documents. As a final act, the […]

The post When Space Isn’t Safe: Inside the European Space Agency’s Massive Cyberattack appeared first on Kratikal Blogs.

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Thales named Growth Index leader in Frost Radar™: Data Security Platforms Report
madhav
Tue, 01/20/2026 - 04:29

Data has always been the backbone of enterprise operations, but the rise of cloud, big data, and GenAI has multiplied its value and, with it, the motivation for attackers. In parallel, regulatory expectations are increasing and evolving. The result is a fragmented security landscape where different tools protect different “data entry points” (databases, SaaS, endpoints, identities, access permissions, and more), often deployed at various times and with inconsistent context and policy enforcement.

Frost & Sullivan’s perspective is clear: data protection must be approached through a data lifecycle lens, where visibility, governance, security, and compliance are applied continuously as data changes type and state.

Data Security

Lynne Murray | Director of Product Marketing for Data Security
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Data has always been the backbone of enterprise operations, but the rise of cloud, big data, and GenAI has multiplied its value and, with it, the motivation for attackers. In parallel, regulatory expectations are increasing and evolving. The result is a fragmented security landscape where different tools protect different “data entry points” (databases, SaaS, endpoints, identities, access permissions, and more), often deployed at various times and with inconsistent context and policy enforcement.

Frost & Sullivan’s perspective is clear: data protection must be approached through a data lifecycle lens, where visibility, governance, security, and compliance are applied continuously as data changes type and state.

That is the strategic imperative behind the modern Data Security Platform (DSP) category, and it is why Thales latest recognition matters.

In the Frost Radar™: Data Security Platforms  benchmarking, Frost & Sullivan shortlisted 11 vendors and evaluated them across 10 Growth and Innovation criteria. The outcome?

Thales is recognized as the leader in the Growth Index and ranks second in the Innovation Index.

Per the report analysts: “Thales is the Growth Index leader on the Frost Radar™ thanks to its unmatched global footprint, diversified customer base, extensive channel ecosystem, and a growth rate that remains largely in line with the overall market’s.”

Frost & Sullivan further attributes this leadership to Thales’ extensive channel ecosystem and market-aligned growth rate.

The DSP imperative: one platform across the data lifecycle

Frost & Sullivan argues that the “DSP” lens helps organizations move beyond acronym-driven point solutions and instead prioritize underlying capabilities tied together by a centralized policy and intelligence engine, enabling the controls to remain consistent across environments and data states.

From this lifecycle-centric view, the ideal DSP delivers core capabilities spanning:

• Data discovery and classification across structured and unstructured data stores.
• Access and identity governance to reduce exposure created by entitlements, identities, and permissions.
• Data protection and control mechanisms (e.g., encryption, tokenisation, masking, policy enforcement).
• Real-time detection and response anchored in rich context and behavioral analytics.
• Compliance enforcement and posture management with continuous assessment as data changes.
• Extended protection for data used in AI systems, including visibility into models and monitoring for anomalous behavior.

Why Thales earned Growth Index leadership

Frost & Sullivan’s assessment highlights the primary Thales differentiator: bringing together two historically separate domains - deep encryption and key management, plus real-time visibility and analytics - into a cohesive DSP approach, so sensitive data remains protected even if perimeter- or identity-based defenses are bypassed.

1. A multilayered architecture anchored in Thales CipherTrust DSP and the Data Security Fabric

Frost & Sullivan describes Thales DSP architecture as anchored by Thales CipherTrust DSP for centralized management of discovery, classification, encryption, key management, and tokenization, and complemented by Data Security Fabric for real-time visibility and risk analytics. This approach delivers “full-spectrum” protection across on-premises, hybrid, and cloud environments.

2. Defense-in-depth at the data layer

A key Innovation differentiator Frost & Sullivan calls out is Thales operational model: even if visibility or governance layers fail, sensitive data remains protected through specialized encryption techniques—an explicit defense-in-depth stance anchored at the data layer.

The report also details Thales capabilities for securing data at rest through centralized encryption, masking, and tokenization supported by unified key management and techniques such as transparent encryption, application protection via SDK, and database column-level encryption.

3. Visibility, analytics, and response—built to reduce false positives

Frost & Sullivan notes that the Data Security Fabric improves detection by combining predetermined rules with rich context, including access patterns and risk profiles derived from application/database traffic, enabling more accurate anomaly detection and more precise blocking. It also supports responses via native actions and automated workflows, with broad integrations across SIEM, XDR, DLP, CNAPP, and ticketing systems, as well as a catalog of over 2,000 pre-built integrations.

4. GenAI and forward-looking roadmap investments

On AI, Frost & Sullivan highlights capabilities to inventory AI models, audit access patterns, and profile LLM behavior for anomalies, alongside additional protections for LLM use cases, including RAG paired with CipherTrust Transparent Encryption. The roadmap direction cited includes deeper DevSecOps integration, more GenAI use cases, and investments toward quantum-resistant encryption.

5. Scale advantages that translate to Growth Index performance

Frost & Sullivan links Thales Growth leadership to execution capacity: global delivery across sectors (including BFSI, manufacturing, retail/eCommerce), amplification from the Imperva acquisition, flexible and scalable pricing (including volume-based licensing), and a go-to-market model combining direct sales with a partner ecosystem of 6,700 technology, service provider, and OEM partners. The report also cites operational depth, including 11 SOCs globally and multilingual support teams.

What this recognition signals for security and business leaders

The DSP market is expanding quickly. Frost & Sullivan cites a 2025 revenue growth expectation of 29.0% and a 2025–2030 CAGR anticipated at 23.2%, driven by the need for enterprises to prioritize unified visibility, consistent controls, and compliance across increasingly complex data estates.

Thales’ placement - #1 in Growth Index and #2 in Innovation Index - is a strong validation of a pragmatic DSP vision: unify governance and intelligence with robust data-layer protection, and extend that model to modern realities like multi-cloud, unstructured data, and GenAI.

Download the full Frost Radar™: Data Security Platform, 2025 report today.

studio THALES BLOG Thales named Growth Index leader in Frost Radar™: Data Security Platforms Report

January 20, 2026

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Learn how Just-in-Time (JIT) provisioning automates user account creation in SSO. Expert guide for CTOs on SAML, SCIM vs JIT, and enterprise IAM security.

The post Just-in-Time (JIT) Provisioning: How Automated User Provisioning Works in SSO appeared first on Security Boulevard.