🚨 Atlas AI Security Breach + Critical WSUS Flaw: The Real ROI of AI-Augmented SOC Teams

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter Topic we cover - AI Agents for SOC: Hype Curve vs. Measurable ROI (continue reading) 

Incase, this is your 1st Cloud Security Newsletter! You are in good company!
You are reading this issue along with your friends and colleagues from companies like Netflix, Citi, JP Morgan, Linkedin, Reddit, Github, Gitlab, CapitalOne, Robinhood, HSBC, British Airways, Airbnb, Block, Booking Inc & more who subscribe to this newsletter, who like you want to learn what’s new with Cloud Security each week from their industry peers like many others who listen to Cloud Security Podcast & AI Security Podcast every week.

Welcome to this week’s Cloud Security Newsletter

The convergence of AI augmentation and traditional security operations reached a critical inflection point this week. While CISA ordered emergency patching of actively exploited Windows Server Update Services vulnerabilities demonstrating how legacy infrastructure remains a vector for supply chain compromise OpenAI's Atlas browser launch simultaneously exposed fundamental architectural challenges in agentic AI security that traditional defenses cannot address.

This week, we feature Edward Wu, Founder and CEO of Dropzone AI, whose recent Cloud Security Alliance research quantifies what many security leaders have only intuited: AI-augmented SOC analysts operate 45-60% faster with measurably higher accuracy than unassisted teams. With patents in anomaly detection using device relationship graphs and eight years building detection and response capabilities for enterprises, Edward brings both technical depth and operational wisdom to the AI SOC transformation conversation.

📰 TL;DR for Busy Readers

• ⚠️ WSUS RCE (CVE-2025-59287) enables unauthenticated RCE treat as supply chain attack vector, not “isolated server issue”, not “just Windows.” Your cloud admin laptops are in scope.

• 🧠 AI-augmented SOC analysts investigate alerts 45-60% faster with higher completion rates CSA research validates operational ROI

• 🏥 Healthcare, public sector, and industrial/OT environments are at active, systemic risk. Ransomware payment rates are down, but attacker sophistication is up.

• 🔐 Microsoft Entra Conditional Access is no longer guidance; it’s policy enforcement. Identity is now a gated control plane.

• 🤖 OpenAI Atlas browser's prompt injection attacks within days of launch expose fundamental AI agent security challenges requiring defense-in-depth

📰 THIS WEEK'S SECURITY HEADLINES

1. . CISA Orders Urgent Patching of Actively Exploited WSUS Flaw in Windows Server

What Happened: CISA issued a binding directive requiring federal agencies to immediately patch a critical Windows Server Update Services (WSUS) vulnerability (CVE-2025-59287) after evidence of active exploitation. The flaw, rated 9.8/10, allows unauthenticated remote code execution through deserialization of untrusted data on exposed WSUS servers. Microsoft shipped an emergency fix post-Patch Tuesday, and security researchers from Huntress, Eye Security, and Shadowserver observed attackers scanning WSUS servers on default ports 8530/8531, with thousands of internet-facing instances identified. Federal agencies face a November 14, 2025 deadline to patch or shut down vulnerable systems.

Why This Matters: WSUS persists in legacy infrastructure even within predominantly cloud organizations, and its privileged access makes it a critical supply chain risk. If attackers achieve SYSTEM-level access on WSUS, they can push malicious updates to downstream Windows fleets enabling lateral movement from a single exposed server into hybrid AD/Azure AD joined endpoints and potentially cloud admin workstations. This represents a software supply chain attack vector that traditional perimeter defenses cannot prevent.

Sources: TechRadar, CISA advisory

2. ⚠️ OpenAI's Atlas Browser Under Fire: Prompt Injection Attacks Surface Within Days of Launch

What Happened: Within days of OpenAI's ChatGPT Atlas AI-powered browser launch, security researchers demonstrated successful prompt injection attacks that manipulated the browser to exfiltrate data, modify settings, and execute unintended actions. Researchers from Brave Security, Johann Rehberger, and others published exploits showing malicious instructions embedded in web pages, Google Docs, and screenshot content could hijack AI agent behavior. OpenAI CISO Dane Stuckey acknowledged that "prompt injection remains a frontier, unsolved security problem" requiring significant adversary investment to exploit.

Why This Matters: This represents the enterprise security community's first major production exposure to agentic AI browser risks. Traditional web security models break when AI agents act with user credentials and the same-origin policy becomes irrelevant because the AI assistant executes with authenticated privileges. Security researcher Johann Rehberger notes that prompt injection "cannot be 'fixed' as soon as a system takes untrusted data and includes it in an LLM query, the untrusted data influences the output." For enterprises considering agentic AI adoption, this requires fundamental architectural changes: capability limiting, data boundary controls, sandboxed execution, least privilege enforcement, and comprehensive logging. Treat agentic browsing as authenticated privilege escalation as a service.

Sources: The Register, Brave Blog,Palo Alto Networks Unit 42 

3. 🎯 Trellix Report: AI-Powered Malware and Nation-State Convergence Accelerate

What Happened: Trellix's October 2025 CyberThreat Report reveals significant AI tool adoption among cybercriminals, with APT28's LameHug the first publicly reported AI-powered infostealer discovered in July 2025. The malware integrates an LLM for dynamic command generation, sending prompts and receiving tailored command sequences for reconnaissance and data exfiltration. Qilin ransomware became the most active group with 441 victim posts (13.45% of activity), while the industrial sector emerged as the primary target with 890 posts (36.57% of attacks). The U.S. accounted for approximately 1,285 victims, representing 55% of geo-identified incidents.

Why This Matters: LameHug represents the industrialization of AI-enhanced threats its operational deployment against Ukrainian organizations demonstrates AI-powered attack tools have transitioned from theoretical to weaponized in state-sponsored arsenals. The convergence of nation-state operations and financially motivated campaigns erodes traditional threat attribution models. In July-August 2025, Iran-aligned threat actors resumed active operations, with over 35 pro-Iranian hacktivist groups coordinating attacks against Israeli targets during the escalated Israel-Iran conflict. For industrial and critical infrastructure operators, this combination of most-targeted sector status with AI-accelerated attacker capabilities demands immediate OT security investment.

Sources:  Industrial Cyber, Trellix CyberThreat Report

4. 📉 Ransomware Evolution: Payments Drop to 23% as Attack Sophistication Increases

What Happened: Ransomware payment rates reached a historic low of 23% of breached companies, while Hornetsecurity's 2025 report shows attacks increased to 24% of organizations (up from 18.6% in 2024), with only 13% paying ransoms. Qilin ransomware escalated rapidly with approximately 700 attacks targeting critical sectors, while new groups like WorldLeaks (rebranded from Hunters International) and Sinobi emerged using pure extortion tactics. Microsoft's Digital Defense Report covering July 2024-June 2025 highlights that over half of cyberattacks with known motives were extortion or ransomware-driven, with 97% of identity attacks being password-based and a 32% surge in identity-based attacks in H1 2025.

Why This Matters: Declining payment rates signal organizational backup and recovery maturity, but attackers are adapting. The 39.5% quarter-over-quarter spike in email-borne malware suggests threat actors pivot toward persistence-based payloads, while 46% of incidents still begin with phishing combined with compromised endpoints and credential theft. For cloud security leaders, the shift from encryption-focused to data exfiltration-first tactics means cloud storage, databases, and SaaS applications are now primary targets rather than just on-premises file servers. This evolution fundamentally changes security architecture requirements from backup-centric to data loss prevention and exfiltration detection capabilities.

Sources: BleepingComputer, Hornetsecurity, Microsoft Blogs

5. 🏛️ Public Sector Under Siege: 196 Government Entities Hit by Ransomware in 2025

What Happened: Approximately 196 public sector entities worldwide fell victim to ransomware campaigns in 2025, with operational downtime costs between 2018-2024 reaching $1.09 billion for government entities alone. The United States experienced the highest number with 69 confirmed victims, followed by Canada (7), the United Kingdom (6), and France, India, Pakistan, and Indonesia (5 each). The most active threat actors include Babuk (43 confirmed victims), Qilin (21), and INC Ransom (18). The first half of 2025 witnessed a 60% increase in government sector attacks compared to the same period in 2024.

Why This Matters: Public institutions represent critical national infrastructure soft targets that often lack resources and technical depth for robust cybersecurity defenses. Services such as police dispatch systems, court operations, and public health portals face immense pressure to restore functionality quickly, creating leverage that attackers exploit through aggressive timelines and public data exposure threats. For private sector security leaders, government agencies are often required technology partners for regulated industries (healthcare, finance, defense contractors), creating supply chain risk. The 60% year-over-year surge suggests threat actors systematically catalog and exploit public sector limited security maturity tactics and tools proven against government targets will eventually target similarly under-resourced private enterprises.

Sources: CyberSecurity News

6. 🏥 Massive Healthcare Imaging Breach: 1.2M Patient Records Exposed

What Happened: SimonMed Imaging, one of the largest outpatient radiology providers in the U.S., disclosed a breach affecting approximately 1.2 million patients. Stolen data includes medical records, diagnostic reports, financial details, and patient identifiers. Reports indicate data has already circulated in criminal channels. The disclosure became public between October 24-27, 2025.

Why This Matters: Healthcare has become nearly fully hybrid: PHI is stored, shared, and analyzed across cloud PACS systems, AI diagnostic pipelines, and third-party billing SaaS. When imaging, billing, and identity data are stolen together, attackers can build extremely high-fidelity synthetic identities and commit high-value fraud at scale. For defenders in any regulated sector (finance, government, critical infrastructure), this warns about vendor blast radius: a single specialized provider can quietly aggregate crown-jewel data across multiple hospitals and insurers. Organizations must revisit BAAs/DPAs with imaging, billing, and AI-diagnostics vendors to ensure right-to-audit on cloud posture, logging, and incident response. Treat PHI/PII fusion data stores (images + financial + ID) as Tier 0 assets with tighter tokenization/encryption than generic "medical records."

Sources: Yahoo, CyberGuy

7. 🤖 AWS Ships "Nova Web Grounding" to Reduce Hallucination in AI Apps

What Happened: On October 28, 2025, AWS announced "Amazon Nova Web Grounding," a Bedrock/Nova capability that automatically retrieves current, attributed information from external sources and injects it into model responses. The positioning focuses on reducing hallucination and increasing traceability in AI-generated answers by attaching live referenced context.

Why This Matters: This advancement addresses more than answer quality it's about evidence and auditability. Most enterprises block or throttle AI assistants because they cannot prove where answers originate, breaking audit, SOX compliance, model risk management, and legal defensibility. Grounded and cited responses become prerequisites for AI copilots making change recommendations to IAM/SCP, firewall rules, and Kubernetes manifests; automated policy generation that can pass audit review; and AI agents acting in production with traceable justification. This lands directly in AI governance and SecOps workflows: organizations cannot approve autonomous changes without attribution. Security teams should ask whether their AI assistants can show provenance for every suggested security control change (network rule, IAM role, token TTL) if not, auto-apply should be prohibited. Start logging AI assistant citations alongside change control tickets to create audit trails when regulators ask, "Why did you trust this AI?"

Source: Amazon Web Services, Blog.

8. 🔐 Microsoft Entra Conditional Access Quietly Becoming Enforcement, Not "Best Practice"

What Happened: Microsoft continues pushing Entra Conditional Access as the mandatory Zero Trust policy engine for accessing Microsoft 365, Azure, and other corporate resources. Recent Microsoft guidance (updated through late September, reinforced through October) ties Conditional Access and MFA enforcement directly to baseline security expectations, and Microsoft has already begun mandating MFA for privileged Azure operations.

Why This Matters: "Identity is the new perimeter" has transitioned from slideware to product policy. Microsoft is moving from "we recommend MFA/CA" to "you will run MFA/CA to touch Azure." For multinational organizations, this effectively outsources access control policy to your cloud provider and creates audit artifacts you don't fully control. Simultaneously, it raises the bar for attackers: token theft, browser session hijack, and SIM swap aren't sufficient if Conditional Access policies enforce device posture, location, and risk signals at authentication time. Security teams should treat Conditional Access configurations like Terraform for firewalls: version them, peer-review them, and run change control on them. Align Conditional Access policies with data classification (production tenant ≠ test tenant). If everything has "global admin if you pass MFA," you've missed the point. Capture Conditional Access decision logs centrally IR teams will need that telemetry when investigating suspicious console activity originating from "trusted" sessions.

Source: Microsoft Learn

🎯 Cloud Security Topic of the Week:

From Hype to Reality: How AI-Augmented SOC Analysts Deliver Measurable ROI

The cybersecurity community has watched AI promises cycle through hype and skepticism for years. Security automation through playbooks and SOAR platforms has existed for over a decade, yet alert fatigue persists and tier-one analyst turnover remains painfully high. The fundamental question isn't whether AI can theoretically improve SOC operations it's whether AI augmentation delivers measurable, reproducible outcomes that justify the investment, complexity, and organizational change required.

This week, we examine findings from Dropzone AI's Cloud Security Alliance research that quantifies AI augmentation's impact on real-world SOC operations. Drawing on insights from Edward Wu whose company recently completed benchmark testing with 148 operational security analysts investigating AWS S3 bucket policy changes and Microsoft Entra ID failed login alerts we explore what AI augmentation actually delivers, where it falls short, and how security leaders should approach SOC transformation in 2025 and beyond.

Featured Experts This Week 🎤

• Edward Wu - Founder & CEO, Dropzone AI

• Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

• Agentic AI: AI systems that can autonomously take actions, make decisions, and adapt strategies based on context rather than following rigid, pre-programmed workflows. In SOC operations, agentic AI goes beyond simple automation to replicate the investigative reasoning and adaptive decision-making of expert human analysts.

• Alert Investigation: The process of analyzing security alerts to determine whether they represent genuine threats, gathering evidence, formulating hypotheses, and validating or invalidating those hypotheses through additional data collection fundamentally resembling detective work in the physical world.

• Prompt Injection: A fundamental security vulnerability in AI systems where malicious instructions embedded in external content (web pages, documents, screenshots) can manipulate the AI agent's behavior to execute unintended actions. Security researchers note this cannot be "fixed" through traditional means because untrusted data inherently influences LLM outputs.

• SOAR (Security Orchestration, Automation, and Response): Traditional security automation platforms that use playbooks rigid, pre-programmed sequences of API calls and actions to automate routine SOC tasks. While useful for deterministic workflows, SOAR lacks the adaptive reasoning required for complex security investigations.

• Tier-One Analyst: Entry-level SOC analysts primarily responsible for initial alert triage, basic investigation, and escalation to senior analysts. These roles face high turnover due to repetitive work, alert fatigue, and limited career development opportunities.

This week's issue is sponsored by Dropzone

New independent research from Cloud Security Alliance proves AI SOC agents dramatically improve analyst performance.

In controlled testing with 148 security professionals using Dropzone AI, analysts achieved 22-29% higher accuracy, completed investigations 45-61% faster, and maintained superior quality even under fatigue.

The study reveals that 94% of participants viewed AI more positively after hands-on use. See the full benchmark results.

📤 Read the Full Study from CSA

💡Our Insights from this Practitioner 🔍

The Vibe Coding Trap: Why AI SOC Requires More Than LLM Prompts(Full Episode here)

Where SOC Automation Actually Delivers Value - Today?

For years, security leaders have heard vendors promise that automation will solve SOC analyst shortage, eliminate alert fatigue, and dramatically accelerate incident response. Yet traditional SOAR platforms delivered underwhelming results. Edward Wu explains why: "The challenge with these type of automation is they are very robotic. From our perspective, the technology has underdelivered compared to the promise that they are made."

The fundamental limitation of traditional security automation lies in its rigidity. Playbook-based systems require security architects to anticipate every possible investigation path and explicitly code API calls, parameters, and decision logic. This works for simple, repetitive tasks but fails for the complex, adaptive reasoning required in security investigations. As Edward notes, "If we look at the type of tasks we're trying to automate within the SOC, like alert investigations being a SOC analyst or investigating alerts require somebody to go through a sequence of steps that actually resembles being a detective in the physical world. You have to look at the evidence, you have to look at, you know, blood stains or fingerprints on the window trims and start to formulate hypothesis and gather additional evidence to validate or invalidate hypothesis."

Quantifying AI Augmentation: The CSA Research Findings

Dropzone AI's Cloud Security Alliance research provides rare, quantified evidence of AI augmentation's operational impact. The study recruited 148 operational security analysts individuals actively working in SOC roles and tested them on two common alert types: AWS S3 bucket policy changes and Microsoft Entra ID failed login attempts. Half the analysts investigated these alerts manually, while the other half used AI augmentation.

The results exceeded expectations. Edward shares: "Maybe the biggest surprise is the actual magnitude of the differences were honestly larger than we originally anticipated. Because keep in mind these... recruited 148 participants. So they are operational, they are in the seat of security analysts. And this is their first time using our product. So we're looking at the impact of AI assistance when it is the first time they have even experienced such technology."

The quantified outcomes speak to both velocity and quality:

• 45-60% faster alert investigation speed for first-time AI-augmented users

• Higher investigation completion rates and accuracy

• Reduced analyst fatigue across tier-one through tier-three skill levels

These improvements materialized immediately without training periods, learning curves, or workflow optimization. For security leaders evaluating AI augmentation ROI, this represents measurable operational impact from day one.

What AI Augmentation Actually Means for SOC Staffing

The natural question following any automation discussion is: will AI replace security analysts? Edward provides a nuanced, realistic assessment: "Can AI SOC analyst automate everything in a SOC? And as a security leader, you can fire everybody in your SOC. That's not going to happen. I do see a world where in the future there will not be that many tier one security analysts as a job role. What we will have is a whole lot more security architects, a whole lot more, you know, security transformation folks."

This shift mirrors transformations in software development. Just as AI coding tools like Cursor didn't eliminate developers but changed their work from writing every line of code to architectural design and solution orchestration, AI SOC augmentation will evolve analyst roles from manual alert processing to higher-level security architecture and strategic decision-making.

Edward elaborates on this parallel: "With AI coding tools, what we have seen is it's a lot more important for software developers to essentially pick up more program management or project management skills, because now with an army of AI coding agents, a single software developer can operate as a team of developers. That means as a human developer, there's actually more quasi-like managerial technical leadership tasks, like you have to divvy up the feature into different components and then you will assign each component to an AI coding agent or Cursor to help you with it."

For SOC operations, this translates to analysts spending less time on repetitive alert triage and more time on:

• Architecting detection logic and response workflows

• Tuning and configuring AI agents for maximum efficiency

• Evaluating investigation quality and identifying edge cases

• Strategic threat hunting and security transformation initiatives

The Complexity and Cost of Building AI SOC Capabilities

Given the hype surrounding generative AI, many organizations consider building internal AI SOC capabilities. "How hard can it be to attach an AI to my SIEM or my log aggregator?" is a common refrain. Edward provides sobering reality checks on this assumption.

"Obviously nowadays it's very cool to start new projects around, hey, you know, I can take this open source library, I can connect it to a couple APIs, and voila, I have an AI SOC analyst," Edward notes. "Based on what we have seen in the field, it's definitely not this easy. In fact, you might have noticed there are close to 30 or 40 different startups building, trying to build similar technologies, but very few actually have working technology, so it's much more difficult than it looks on paper."

The technical challenges extend far beyond connecting APIs to large language models. Edward emphasizes the core difficulty: "The biggest challenge when building AI agents for security is how do you manage large language models? How do you find the right balance between allowing large language models to improvise and adapt while keeping them within certain guardrails so they can offer trustworthy and deterministic outputs. And that's actually very difficult."

The financial reality underscores this complexity. Edward shares: "At Dropzone, if we look at 2020, by the end of 2026, Dropzone would have spent close to $20 million purely on R&D to build this technology. Unless as a security organization you are maybe allocating five or $10 million of budget to build such a technology, you are probably not going to be able to get it right."

Organizations attempting internal builds also face talent challenges. Building agentic AI systems requires not just security expertise but also:

• Data science and machine learning engineering capabilities

• Data pipeline architecture and management

• LLM fine-tuning and prompt engineering specialization

• Iterative testing and validation frameworks

For most organizations, buying mature AI SOC technology delivers faster time-to-value than multi-year, multi-million-dollar internal development efforts.

Who Benefits Most from AI-Augmented SOC Operations

AI augmentation isn't equally valuable across all organizational contexts. Edward identifies two primary beneficiary groups based on Dropzone's customer base.

The first group comprises mid-market to enterprise organizations (typically 200+ employees) with internal SOC teams. "Those are the folks who are leveraging internal resources. They have full-time security analysts," Edward explains. "Those security teams will definitely benefit from our technology."

However, the second group represents perhaps the more transformative use case: managed security service providers (MSSPs) and managed detection and response (MDR) providers. Edward notes: "AI augmentation actually drastically increase the quality of the security services that service providers like MSPs or MDRs can offer. So this is where I don't think with AI, like if you are an organization of 200 employees, I still think security service providers are the best way to get the initial set of security protections and capabilities. But now with AI augmentation, you can get a whole lot more from your security service providers."

This insight has important strategic implications. Smaller organizations that historically couldn't justify full-time SOC analysts can now access sophisticated security operations through AI-augmented service providers at price points previously impossible. The service provider economic model where a single analyst team serves multiple clients combines powerfully with AI augmentation that multiplies each analyst's investigation capacity 1.5-2x.

Training and Skill Development in the AI SOC Era

As security leaders plan year-end training budgets, understanding which skills matter in AI-augmented environments becomes critical. Edward's guidance mirrors software development transformations: "A lot of that is, again, using software development as analogies. With AI coding tools, what we have seen is it's a lot more important for software developers to pick up more program management or project management skills."

The specific capabilities Edward recommends SOC teams develop include:

• Technical leadership: The ability to function as a tech lead, dividing complex security projects into smaller, manageable components that AI agents can execute

• Quality assessment: Developing the judgment to distinguish high-quality from low-quality AI-generated investigations and recommendations

• AI agent configuration: The ability to coach, tune, and configure AI solutions to achieve maximum efficiency within organizational context

• Strategic architecture: Moving beyond tactical alert response to designing detection strategies, response workflows, and security transformation initiatives

Training programs should shift focus from teaching analysts "how to investigate alerts" to "how to architect, oversee, and optimize automated investigation systems." This parallels how software engineering education now emphasizes system design, API integration, and solution architecture over memorizing syntax.

Practical Implementation: What Security Leaders Should Do Now

For security leaders evaluating AI SOC augmentation, Edward's research and operational experience suggest several concrete actions:

1. Test AI augmentation with realistic scenarios. Don't rely on vendor demos. Request proof-of-concept evaluations using your actual alert types, your SIEM data, and your analysts. The CSA research used common alerts (S3 bucket policy changes, Entra ID failed logins) precisely because they represent real-world SOC workloads.

2. Measure velocity and quality, not just speed. AI augmentation should improve both investigation speed (45-60% faster) and completion rates/accuracy. If a solution only improves speed by generating low-quality investigations, it will create downstream problems rather than solving them.

3. Realistically assess build vs. buy economics. Unless you can allocate $5-10M and multi-year timelines for AI SOC development, buying mature technology will deliver faster ROI. The 30-40 startups attempting to build this technology with only a few succeeding demonstrates the difficulty.

4. Evolve SOC career paths proactively. Communicate to your teams that tier-one analyst roles will evolve, but that security careers aren't disappearing; they're becoming more strategic, architectural, and impactful. Invest in training that prepares analysts for these elevated responsibilities.

5. For smaller organizations, prioritize AI-augmented service providers. If your organization has fewer than 200 employees and lacks dedicated SOC staff, leverage MSSPs and MDR providers that use AI augmentation. You'll access enterprise-grade capabilities at small-business price points.

6. Establish AI agent governance frameworks now. As agentic AI systems gain more autonomy, you need guardrails, approval workflows, and audit trails. Start simple: require human approval for any AI-suggested changes to production systems, log all AI agent actions, and establish review processes for AI investigation quality.

The transition to AI-augmented SOC operations isn't theoretical; it's happening now, with measurable outcomes that validate the investment. Security leaders who approach this transformation strategically, grounded in evidence rather than hype, will build more resilient, scalable, and effective security operations for the hybrid cloud era.

RELATED RESOURCES

• Cloud Security Alliance: Dropzone AI SOC Benchmark Research Report   Comprehensive analysis of AI augmentation impact on SOC analyst performance with 148 operational participants

• Dropzone AI Test Drive   Ungated, live environment demonstrating AI SOC analyst investigations across different alert types (Dropzone.ai)

• CISA Known Exploited Vulnerabilities Catalog   Authoritative list of actively exploited vulnerabilities requiring immediate patching (cisa.gov/known-exploited-vulnerabilities)

• Microsoft Entra Conditional Access Documentation   Implementation guidance for Zero Trust identity controls in Azure and Microsoft 365 environments

• Trellix CyberThreat Report October 2025   Analysis of AI-powered malware evolution and nation-state threat convergence

• Johann Rehberger's Prompt Injection Research   Security researcher's analysis of fundamental AI agent vulnerabilities and mitigation strategies

• AWS Security Best Practices for AI/ML Workloads   Cloud-native guidance for securing AI model deployments and data pipelines

• MITRE ATT&CK Framework for Cloud   Comprehensive matrix of cloud-specific tactics, techniques, and procedures (attack.mitre.org)

Related Podcast Episodes 🎧

Question for you? (Reply to this email)

 🤖 Is your SOC ready to trust AI agents for autonomous alert investigation what's the first use case you'd pilot?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

📬 Want weekly expert takes on AI & Cloud Security? [Subscribe here]”

We would love to hear from you📢 for a feature or topic request or if you would like to sponsor an edition of Cloud Security Newsletter.

Thank you for continuing to subscribe and Welcome to the new members in tis newsletter community💙

Peace!

Shilpi Bhattacharjee

Was this forwarded to you? You can Sign up here, to join our growing readership.

Want to sponsor the next newsletter edition! Lets make it happen

Have you joined our FREE Monthly Cloud Security Bootcamp yet?

checkout our sister podcast AI Security Podcast