Author, Creator & Presenter: Ivan Banov
Our thanks to BSidesCache for publishing their Creators, Authors and Presenter’s outstanding BSidesCache 2025 content on the Organizations' YouTube Channel.
The post BSidesCache 2025 – How To Hide In Plain Sight: Next-Level Digital Privacy appeared first on Security Boulevard.
The threat environment facing the United States is growing more complex and interconnected. Executive Order 14186 identifies the threat of attack by ballistic, hypersonic, and cruise missiles, along with other advanced aerial attacks, as "the most catastrophic threat facing the United States." In response, the U.S. is pursuing Golden Dome for America, a next-generation missile defense architecture intended to defend the homeland and critical infrastructure against foreign aerial attacks.
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Learn how exposed Ollama servers can allow unauthorized model access, prompt abuse, and GPU resource consumption when LLM inference APIs are publicly accessible.
The post Exposed Ollama Servers: Security Risks of Publicly Accessible LLM Infrastructure appeared first on Indusface.
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Akamai researchers saw a 245% spike in cyberattacks in the first two weeks after the start of the U.S. and Israeli war against Iran as Iranian nation-state groups and independent hacktivists launch increasingly decentralized and destructive cyberattacks, which are expected to increase as long as the kinetic battle continues.
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NEW YORK, Mar. 17, 2026, CyberNewswire—Orchid Security, the company bringing clarity and control to the complexity of enterprise identity, today announced it has been recognized as a Representative Vendor in Gartner’s Market Guide for Guardian Agents, … (more…)
The post News alert: Orchid Security brings Zero-Trust to AI Agent identities, earns Gartner recognition first appeared on The Last Watchdog.
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Company Profile Charm Security (hereinafter referred to as Charm) is an innovative security company focused on preventing and solving fraud and deception using Agentic AI technology. Founded in January 2025, the company has set up offices in Tel Aviv, Israel and New York, USA. With a core focus on financial security, it has become an […]
The post RSAC 2026 Innovation Sandbox | Charm Security: AI Anti-Fraud Platform for New Types of Fraud appeared first on NSFOCUS, Inc., a global network and cyber security leader, protects enterprises and carriers from advanced cyber attacks..
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NEW YORK, Mar.17, 2026, CyberNewswire — GitGuardian, the security leader behind GitHub’s most installed application, today released the 5th edition of its “State of Secrets Sprawl” report, documenting how mainstream AI adoption in 2025 reshaped software delivery and accelerated the … (more…)
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Secure your AI agents with lattice-based IAM. Learn how ML-KEM and ML-DSA protect Model Context Protocol (MCP) from quantum threats and puppet attacks.
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Mar 17, 2026 - Jeremy Snyder - The State of AI Security: Moving Beyond TheoryThe biggest shift evident at the [un]prompted AI Security Practitioners Conference was the move from purely theoretical discussions about "what could go wrong" to concrete, battle-tested methodologies for "what is going wrong and how we fix it." It’s clear that AI security is rapidly evolving, from initial employee DLP use cases, to organization-wide focus around securing all THINGS A.I..This Week in AI SecurityWe published an episode of our This Week in AI Security Podcast right after the event, which you can watch here below.In the episode, I shared some of my key thoughts around several major themes:LLMs for Vulnerability Discovery and the Zero-Day Clock: Many researchers shared information on using LLMs to identify zero days, malware, and source code vulnerabilities. The most striking observation was the dramatic acceleration of the "meantime to availability of an exploit," which has reduced from months to hours. This is a "call to arms" for the cybersecurity industry and raised the question of whether automatic patching is now required.Defensive Automation and Agentic Infrastructure: I heard presentations from companies like Google, OpenAI, and Meta about their security strategies, tooling, and efforts to leverage AI agents for security automation.New Attack Surfaces: This area included discussions on indirect prompt injection, new attack vectors in AI-automated systems like KYC pipelines and image recognition (OCR embedded in LLMs), and the vulnerabilities and legal implications of ubiquitous AI notetakers (meeting assistants).Prompt as Code (Conceptual Highlight): To me, the concept of "thinking about the prompt as code" from the Google Gmail team was one of the most interesting conceptual points, emphasizing the need to apply secure coding and hygiene practices to the prompt itself, as it serves as an instruction set.Real-World Case Studies: I noted good real-world case studies from various firms (Trail of Bits, Wiz, others), including the use of multi-agent triage to uncover breaches.Overall, huge kudos to the team over at Knostic!But that’s not all…There were a number of other topics that I didn’t have enough time to cover in the 15-minute episode. Here are some of my thoughts below. Operationalizing Threat Modeling for LLMsOne theme was the urgent need for threat modeling tailored specifically to Large Language Models (LLMs) and generative AI systems. Traditional application security models often fall short, failing to account for the unique attack surface introduced by model weights, training data pipelines, and prompts themselves.Key speaker sessions highlighted a new approach focusing on three main challenges:Model Theft & Extraction: Protecting intellectual property embedded in the model itself.Inference-Time Attacks (Prompt Injection, Evasion): Mitigating threats during real-time use.System-Level Integration Risks: Addressing vulnerabilities introduced when LLMs connect to external tools (RAG, code execution).A Shift in Attack Vectors: Focus on Evasion and MisuseWhile Prompt Injection remains a foundational concern, the conversation has matured to address more subtle and potentially damaging attack vectors.Adversarial Evasion TechniquesSeveral talks detailed advanced adversarial examples designed not just to trick the model into an undesirable output, but to subtly shift its behavior over time or bypass safety filters without obvious jailbreaking language. This requires a defensive posture that looks beyond simple keyword blocking and into semantic understanding and anomaly detection on input and output data.Misuse and Abuse by DesignThe focus is increasingly on how malicious actors can misuse the powerful capabilities of an AI system, even when it's technically operating "as intended." For example, using a coding assistant LLM to generate highly optimized malware code or leveraging an RAG system to exfiltrate proprietary data through cleverly crafted queries. This necessitates integrating "red teaming" early in the development lifecycle, simulating real-world abuse scenarios before deployment.The Tooling Landscape: What Practitioners Are UsingThe conference provided a fantastic overview of the tools that are actually making a difference in AI security labs today. The consensus is that no single tool provides a complete solution, so a layered defense strategy is essential.The Rise of Defense-in-Depth for AIThe core message is the need for an approach that includes:Application Layer: Prompt engineering guidelines and specific guardrails.Middleware/Proxy Layer: Dedicated AI security tools intercepting API calls for validation, sanitization, and logging.Model Layer: In-model defenses (e.g., constitutional AI, fine-tuning for robustness) and continuous monitoring of model performance and drift.Looking Ahead: The Human Element and Future ChallengesBeyond the technical deep-dives, the most engaging discussions centered around the future. Below are some of my thoughts from conversations with AI security leaders that I had at the event:We’re still in the earliest stages of securing AI adoption.We only know the challenges presented today, and we haven’t solved all of them yet. There’s almost certainly some Rumsfeld Matrix “unknown unknowns” that will emerge in the near-term and medium-term future. Everyone seems to agree that 2026 is the year that we start expanding the scope of needed AI security platforms from employee-focused to everything-focused.Humans are needed. In fact, more humans are needed than ever before, or than we currently have. I see an exciting future ahead in securing AI for companies everywhere.
The post [un]prompted: Key Insights from the AI Security Practitioners Conference – FireTail Blog appeared first on Security Boulevard.
An N-day vulnerability in Microsoft Word exposes nearly 14 million assets. Attackers can exploit this flaw to bypass security prompts, enabling deployment of malware and establishing persistent access without triggering user warnings.
Key takeaways:Tenable conducted an exposure data analysis across seven Tier 1 countries; Israel, the United States, Bahrain, Kuwait, the United Arab Emirates, Qatar, and the Kingdom of Saudi Arabia, following Operation Epic Fury. Our asset exposure analysis identified over 15.5 million affected assets across the Tier 1 countries, with the United States accounting for 15.4 million of them. We identified that a Microsoft Word N-day, CVE-2026-21514, accounts for nearly 14 million exposed assets across the seven target countries.
This research demonstrates that threat intelligence focusing solely on conflict-specific exploitation patterns can systematically underweight the most broadly impactful vulnerabilities. By applying exposure management principles, organizations can look beyond the geopolitical narrative to patch the largest exploitable attack surface and reduce the risk of compromise by advanced persistent threats (APTs).
FAQWhat is CVE-2026-21514?
CVE-2026-21514 is a security feature bypass vulnerability in Microsoft Word. It was assigned a CVSSv3 score of 7.8 and rated important.
When was CVE-2026-21514 first disclosed?
Microsoft disclosed CVE-2026-21514 on February 10, 2026, as part of its February 2026 Patch Tuesday release.
Was CVE-2026-21514 exploited in the wild?
Yes, Microsoft confirmed active exploitation in the wild prior to the patch release. The vulnerability was discovered and reported by the Google Threat Intelligence Group, Microsoft Threat Intelligence Center (MSTIC) and Microsoft Security Response Center (MSRC).
Does exploitation require user interaction?
Yes, the user must open a malicious Word document. However, the Preview Pane is not an attack vector. Once the malicious document is opened, no further user interaction is required. The exploit bypasses the security prompts that would normally alert the user to danger. Unlike traditional macro-based attacks that trigger "Enable Content" prompts or Protected View warnings, CVE-2026-21514 executes its payload silently. The user sees the document content; the attacker gets code execution.
This distinction is critical for defenders: security awareness training that teaches employees to "not click the yellow bar" does not protect against this vulnerability, because the yellow bar never appears. The document simply opens and the payload fires.
What could an attacker do if they successfully exploit CVE-2026-21514?
Successful exploitation enables an attacker to silently bypass document security controls and execute arbitrary code with the privileges of the logged-in user. The impact spans the full spectrum: data theft, file modification, malware deployment and persistent access establishment.
What is the severity of CVE-2026-21514?
Microsoft Word is a ubiquitous enterprise word processing application deployed across virtually every industry vertical and government agency worldwide, and a core component of several Microsoft products including 365 Apps for Enterprise, Office LTSC 2021, Office LTSC 2024, and Office LTSC for Mac 2021 and 2024.
The operational severity is exceptionally high despite the 7.8 CVSSv3 score. Three factors converge to make this the highest-priority vulnerability in the current threat landscape: the massive scale of exposure (nearly 14 million affected assets), confirmed active exploitation as a zero-day and precise alignment with the phishing delivery methodology of Iran-nexus APT groups. The CISA KEV mandate required federal agencies to patch by March 3, 2026.
Why is this vulnerability noteworthy?
This flaw allows an attacker to bypass Object Linking and Embedding (OLE) and Mark-of-the-Web (MotW) protections in Microsoft Word. The vulnerability stems from improper validation of security decisions based on untrusted inputs (CWE-807). Attackers manipulate the internal XML structure of a crafted Word document to convince the application that a malicious OLE object is trustworthy, causing it to execute without displaying the "Enable Content" prompts or Protected View warnings that users are trained to watch for.
It represents the largest single attack surface in potential cyberattacks since the Operation Epic Fury conflict began, and aligns with the phishing tradecraft of Iranian APT groups. MuddyWater, for example, routinely delivers malware via malicious Office documents as seen in its Operation Olalampo campaign.
What is the exposure profile for CVE-2026-21514?
Tenable’s exposure data analysis revealed 13,988,520 affected assets for this specific vulnerability across the seven target regions, making it the largest single vulnerability exposure for potential cyberattacks since the conflict began by two orders of magnitude.
Our exposure data analysis shows that this CVSSv3 7.8 vulnerability represents a larger operational risk than CVE-2025-32433, an Erlang SSH remote code execution vulnerability with a CVSSv3 score of 10.0 affecting 296,174 assets. This is because CVE-2026-21514 has 47 times more exposed assets, confirmed active exploitation, CISA KEV status and direct alignment with the dominant Iranian APT delivery methodology. This is a clear example of why CVSS scores measure theoretical severity while exposure data measures actual attack surface.
How does CVE-2026-21514 relate to Iranian threat actors?
State-sponsored actors like MuddyWater use malicious Microsoft Office documents to deliver rapid-iteration malware. Between late January and early March 2026, MuddyWater deployed six distinct malware families across multiple campaigns, including the CHAR backdoor (Rust-based with Telegram command and control (C2)), GhostBackDoor (interactive shell), GhostFetch (first-stage downloader), HTTP_VIP (custom downloader with Flask/SQLite C2), Dindoor (Deno-based JavaScript backdoor using "Bring Your Own Runtime" evasion) and Fakeset (Python backdoor). The convergence of AI-assisted malware development tempo with the potential use of an N-day that silently bypasses document security controls represents a threat multiplication effect.
How does this vulnerability relate to the broader Operation Epic Fury threat landscape?
Operation Epic Fury has produced the first true hybrid war where kinetic infrastructure destruction and cyber operations are executing simultaneously at scale. The exposure data analysis reveals that CVE-2026-21514 is the single largest exploitable attack surface across all seven target countries, yet it received less analytic attention in initial threat intelligence products than the IP camera exploitation chain (which enables kinetic targeting) and the Fortinet perimeter chain (which provides direct network access).
The exposure data fundamentally reshapes prioritization. The IP camera campaign is the most operationally novel finding of the conflict, and a single compromised camera at a refinery can enable a missile strike that shuts down 20% of global liquified natural gas (LNG) supply. But by asset count, CVE-2026-21514 (13,988,520 assets) dwarfs the next most exposed vulnerability, CVE-2024-30088 (991,920 assets), by a factor of 14. Organizations that patch cameras but not Word are defending against the headline threat while leaving the largest door open.
What is the exposure across industry verticals?
The exposure data reveals significant concentration in verticals that are explicitly targeted by Iranian actors during Operation Epic Fury. Healthcare is the second most exposed vertical at 1.75 million affected assets, directly relevant given that Handala (the public-facing persona of Iran's Void Manticore) executed a wiper attack against medical technology company Stryker on March 12, reportedly destroying 200,000+ devices across 79 countries. Government follows at 1.1 million, Retail at 1.4 million and Manufacturing at 1.1 million. The "Other" category leads at 1.8 million.
What is the geographic distribution of exposure?
The geographic concentration is the most striking finding in the exposure data. The United States accounts for 15,447,390 of the 15,529,792 total affected assets–99.4% of the exposure. The UAE follows at 60,598, Saudi Arabia at 12,391, Israel at 9,229 and Kuwait at 184. This means U.S. organizations, particularly in healthcare, government, retail, and manufacturing, carry a disproportionate share of the exploitable surface, even though Gulf states face the most acute conflict-specific targeting.
Are patches or mitigations available for CVE-2026-21514?
Yes. Microsoft released security updates on Feb. 10, 2026, as part of its February 2026 Patch Tuesday. Updates are available via Click-to-Run for Windows versions and version 16.106.26020821 or later for Mac systems.
CISA mandated federal agencies patch by March 3, 2026. However, enterprise Word deployments are difficult to patch quickly due to change control processes, update ring configurations and the sheer scale of Microsoft 365 deployments. Non-federal organizations have no binding mandate and many remain unpatched.
Do end users need to take any steps to address this in their environment?
Yes. Organizations must take immediate action to mitigate this vulnerability. Defenders should prioritize the following steps:
For organizations using Microsoft Intune for endpoint management, verify Intune for unauthorized policy changes. Handala's Stryker attack demonstrated that compromising an Intune console can be used to push destructive commands to hundreds of thousands of devices.
What is the current defender window?
Unit 42 assessed that Iran's internet connectivity dropped to 1-4% following the opening strikes of Operation Epic Fury, which is likely limiting the ability of state-sponsored actors to coordinate sophisticated operations in the near term. This creates a finite window, measured in days to weeks, for defenders to harden infrastructure before Iranian connectivity recovers and pre-positioned access is activated at scale. Every day that passes without patching CVE-2026-21514 is a day ceded to adversaries who have already demonstrated both the capability and intent to cause destructive harm at scale.
Which Tenable products can be used to address this vulnerability?
Tenable One Exposure Management Platform provides unified visibility across IT, cloud, identity, and OT environments, enabling security teams to identify CVE-2026-21514 exposures alongside other critical flaws in a single prioritized view. Tenable Vulnerability Management and Tenable Security Center include detection plugins for CVE-2026-21514 and all other CVEs referenced in the Operation Epic Fury analysis.
A list of Tenable plugins for this vulnerability can be found on the individual CVE page for CVE-2026-21514 as they’re released.
This link will display all available plugins for these vulnerabilities, including upcoming plugins in our Plugins Pipeline.
By correlating vulnerability data with asset context and threat intelligence, organizations can operationalize exposure management to find, prioritize, and secure vulnerable Microsoft Word instances at scale.
Get more informationJoin Tenable's Research Special Operations (RSO) Team on Tenable Connect for further discussions on the latest cyber threats.
Learn more about Tenable One, the Exposure Management Platform for the modern attack surface.
The post FAQ on CVE-2026-21514: OLE bypass N-Day in Microsoft Word appeared first on Security Boulevard.
Is Your Organization Overlooking the Nuances of Non-Human Identity Management? Spotlighting the Intricacies of Non-Human Identity Management How often does your security team delve into the complexities of Non-Human Identity (NHI) management? When organizations increasingly shift operations to the cloud, managing these machine identities becomes crucial for maintaining robust security. But what exactly are NHIs, […]
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How Can Non-Human Identities Revolutionize Cloud Security? Have you ever considered how machine identities play a role in your cloud security strategy? When organizations increasingly rely on cloud environments, the significance of securing Non-Human Identities (NHIs) and Secrets Management has become a priority. Despite the varied organizational structures across industries like financial services, healthcare, or […]
The post What makes NHIs crucial for secure cloud environments appeared first on Entro.
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What Is the Impact of Non-Human Identities on Cloud Security? When dealing with cyber threats, how secure is your AI? Non-Human Identities (NHIs) have emerged as pivotal resources, particularly in managing protected AI environments such as Agentic AI. NHIs, essentially machine identities, are integral in safeguarding confidential information across multiple sectors, including financial services, healthcare, […]
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Sonatype Security Research has identified two hijacked npm packages in the React Native ecosystem that receive more than 30,000 downloads collectively per week and were modified to deliver multi-stage malware. Sonatype is tracking the malicious packages as sonatype-2026-001153.
The post Hijacked npm Packages Deliver Malware via Solana, Linked to Glassworm appeared first on Security Boulevard.
Look, I’m not a developer, and the last time I truly “wrote code” was probably a good number of years ago (and it was probably Perl so you may hate me). I am also not an appsec expert (as I often remind people).
Below I am describing my experience “vibe coding” an application. Before I go into the details of my lessons — and before this turns into a complete psychotherapy session — I want to briefly describe what the application is supposed to do.
Anton’s vibe app screenshotWe have a podcast (Cloud Security Podcast by Google), and I often feel that old episodes containing useful information aren’t being listened to and the insights from them go to waste. At the same time, for many organizations today, the answer to their current security problems may well have been discussed and solved in 2021. This may be strange to some, but for many organizations, the future is in the past. Somebody else’s past!
So I wanted “a machine” that turns old episodes into role-specific insights, without too much work by a human (me). This blog is a reflection on how things went.
First, my app is using public data — namely podcast transcripts and audio — to create other public data (social media posts). Since the inputs and outputs are public, this certainly made me at peace with vibe coding. Naturally, I needed to understand how the app would be coded, where it would live and what I should do to make it manifest in the real world. So I asked Gemini, and it suggested I use AI Studio by Google, and I did (non-critically) exactly that.
When I started creating the app, the question of storage immediately came up. Jumping a little bit ahead, you will see that authentication / credentials and storage were two security themes I reflected on the most.
You want to read a file from storage, but what storage? More importantly, whose storage? At this point, I had my first brush with anxiety of the “vibe process.” I didn’t want to just vibe code without a full understanding of the data access machinery. I immediately said, “No, I don’t want to store data in my Google Drive using my credentials.” I just didn’t trust it.
In fact, I didn’t trust the app with any credentials for anything — work or personal — at all! Given that I have public data, I decided to store it in a public wed folder. AI Studio suggested ways to store data that people might not fully understand, and this is my other reflection: If I’m not a developer, and I don’t know the machinery behind the app, how do I decide? These decisions are risk decisions and “a citizen vibe coder” is very much not equipped to make them. Well, I sure wasn’t.
So what are the security implications of the decisions a developer makes — sometimes guided by AI and sometimes on their own? Can I truly follow an AI recommendation that I don’t understand? Should I follow it? If you don’t understand what happens, I can assure you, you certainly do not understand the risks!
As a result, I did not trust the app with any credentials or authenticated access. Of course, a solution may have been to use throwaway storage with throwaway credentials, but I think I do not need this in my life... Anyhow, many actions that you take during vibe coding, whether suggested by AI or not, have security implications.
In addition, the app interacts with the environment. If the app is being built in a corporate environment, it interacts with corporate security “rules and tools”, and some things you may want to do wouldn’t work. I’m not going into details, but I had a couple of examples of that. If you vibe code at work and you are doing it through, let’s say, shadow AI, there will be things your AI (and you) would want to do, but your employer security would not allow. And often with good reasons too! So you ask AI for more ways and hope it won’t say “just disable the firewall.”
The next conundrum, apart from storage, was output quality. What about quality and those hallucinatory mistakes? Now, I know my app uses an LLM to condense a summary of the podcast transcript into brief insights for social media. And before my app runs, another LLM turns MP3 into text. And it also uses an LLM to make the visual summaries. So, the question is: who handles the mistakes, and how?
For example, I tried to use a certain “well known” model to create a visual summary. Of course, the visual summary was incredibly accurate in most cases, but sometimes “mistakes were made” and words were corrupted (“verifigement” happened to me in one case). If an LLM powered tool can do something, it does not mean it will do it equally well every time (unless you build validators AND the things that you need to do can in fact be validated). So validate!
Further, I read somewhere that the process for dealing with AI mistakes is different from the process for dealing with human mistakes. I am sure I could write another module for the app to check if an image has correct text or add another validation technique, but it is interesting that I faced this very quickly.
Thus I have to deal with “AI-style mistakes”, and I cannot solve them by having a human review everything. I can tell you right away, even from my small project, that having a human review is a non-starter. It’s theoretically correct, but practically won’t happen. It absolutely will not happen if you take the koolaid and transform your business process to be “AI native.” Having humans review boring tasks like checking image text is completely insane. That’s not going to fly. HITL is DOA (for these tasks).
So: storage, credentials, trust, and quality all came up. Another decision arose when I needed to store intermediate results of my insight generation. Again, trust issues surfaced because data storage. AI Studio suggested choices, I asked AI about pros/cons, and made the decision. Again all these decisions are risk decisions.
Finally, certain mistakes come up all the time, repeatedly, and I have to tell AI Studio to write things multiple times because it doesn’t always “get” it (example: my podcast episode URLs). This is another lesson: sometimes it takes multiple prompts, and constant reminders (say to validate the links)
All in all, I’ll continue to experiment — got more ideas that I want. Here are some outputs of my app…
Anton vibe app UXNow the explicit lessons for those who need this crisp and actionable:
1. You Make Implied Security Decisions with Every Prompt
When you “vibe code,” you aren’t just describing features; you are making risk and security decisions. If you ask an AI to “save this data,” and you don’t specify how or where, the AI may choose the path of least resistance — usually a public bucket or a local file with cleartext credentials. In the world of AI-generated code, silence is a security decision.
2. Credentials and Storage: The Boring Stuff is Still the Hard Stuff
Storage and credentials were the key themes for me. This is the great irony of modern development: AI can write a complex LLM orchestration layer in seconds, but it may struggle to help a novice set up a secure, encrypted secrets manager. The “plumbing” of security remains the primary friction point.
3. AI Mistakes Require a New Response Model
Traditional QA seems designed for deterministic human error. AI “style mistakes” (like corrupted words in a visual summary) are stochastic and weird. And common! Human review is a “non-starter” for these tasks. Security and quality validation for AI-generated content must itself be automated (AI-on-AI validation) because humans simply won’t do the “deathly boring” work of checking verbatim accuracy at scale. Turtles all the way down can happen to you.
4. Corporate Guardrails vs. AI Ambition
The AI you vibe code with may not know your corporate policy. It will suggest “awesome” features that would immediately trigger a compliance violation. A few times while vibe coding, I heard a subtle lawyercat meowing in the air duct… When vibe coding in a corporate environment, you quickly hit the wall where “what the AI wants to do” meets “what security allows.” This reinforces the need for platform-level guardrails rather than just merely developer education.
5. Public Data is the Only “Safe” Vibe
My “peace of mind” came from the fact that your inputs and outputs were already public. To me, this is the only way to vibe code safely without a full understanding of the underlying security stack. The moment you move from “public podcast audio” to “proprietary customer data,” the risk model shifts from “fun experiment” to “data breach.”
Anyhow, this was my mildly-AI-assisted stream of vibe consciousness.
Enjoy the show! Now with video!
Anton’s Vibe Coding Experience: A Reflection on Risk Decisions was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.
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96.5% of orgs run AI on production databases. Learn why database change governance matters more than model risk, and how leading teams govern AI-speed change.
The post AI is Already in Your Database: The Real Risk is How You Govern Change | Liquibase appeared first on Security Boulevard.
I'm excited to announce that I will be leading the DCIM Leadership Workshop at Data Center World AFCOM 2026 this April, taking over from Bill Kleyman. For the past nine years, Bill has set the gold standard for this workshop, making it a crucial event for data center leaders to tackle real-world challenges. In this three-hour session, we'll dive deep into managing AI workloads, liquid cooling integration, and the shift toward autonomous operations. I'm honored to continue Bill's legacy and look forward to having honest, challenging conversations that push our industry forward.
The post The Now, New and Next in Data Center Infrastructure Management appeared first on Hyperview.
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BSidesSLC
Author, Creator & Presenter: Andrew Crottym - Warrant Officer (Cyber Warfare), United States Army Reserve
Our thanks to BSidesCache for publishing their Creators, Authors and Presenter’s outstanding BSidesCache 2025 content on the Organizations' YouTube Channel.
The post BSidesCache 2025 – From Law Enforcement To Cybersecurity: Building Skills That Matter appeared first on Security Boulevard.
Microsegmentation has moved well beyond a narrow infrastructure conversation. Today, teams need to enforce policy across cloud workloads, data centers, user endpoints, containers, and OT and IoT environments without creating more operational friction than security value. That broader requirement is exactly why we built the ColorTokens Xshield Enterprise Microsegmentation Platform™ the way we did. It is also […]
The post ColorTokens Once Again Named a Leader and Outperformer in the 2026 GigaOm Radar for Microsegmentation appeared first on ColorTokens.
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The phishing site it is not affiliated with Igloo Inc or Pudgy Penguins, but is designed to lure fans and steal their crypto passwords.
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