Cybersecurity Risks

None will ever be able to stop Shadow AI! When people find an AI tool that helps them work faster, think better, or save time, they use it. That is what good people do. They look for leverage. Banning this is not a strategy. It is just an invitation to hide it, which is worse. So, what can we do? In my discussion with Stephen Schmidt, Chief Security Officer at Amazon, one message came through very clearly: 👉 The role of security is no longer to stop Shadow AI—because it is impossible. 👉 The role of security is to make AI safe, visible, and controlled. 👉 To know what is being used, where it is installed, what it can access, and where the data goes. That is the real shift. Because the biggest danger with AI is often not the intelligence. It is the permission. The moment an agent gets broad access to your files, systems, or sensitive data, your risk changes completely. So the question is not: “How do we stop Shadow AI?” The question is: “How do we make sure AI does not operate in the shadows?” That means four things: 1️⃣ Visibility: Create an inventory of the AI tools and agents people are actually using. 2️⃣ Boundaries: Run agents in isolated environments, not freely on laptops or production systems. 3️⃣ Permissions: Give agents only the minimum access they need, nothing more. 4️⃣ Traceability: Log actions so you know what the agent did, what data it touched, and who triggered it. This is where many leaders get it wrong. They think control means restriction. It does not. Real control means creating an environment where AI can be used fast, safely, and in the open. The companies that try to ban AI will lose visibility. The companies that learn to govern it will gain trust, speed, and advantage. You cannot stop Shadow AI. But you can stop unmanaged AI. 💥 Curious to learn more: https://lnkd.in/eubr-VpH How is your organization dealing with this today? #AWSAmbassador #AI #AgenticAI #Cybersecurity #Leadership #FutureOfWork

The report I HATE the most is one I call the VENDOR WALL OF SHAME. It serves as a daily reminder of how far core technology suppliers still have to go in making SECURITY a first principle and not an afterthought.   At Visa, we executed 170,000+ production changes during our last fiscal year just to patch vendor security vulnerabilities discovered AFTER major technology vendors had already certified their products as gold code, ready for customers like Visa to use.   Every new vulnerability in core underlying software and hardware systems (e.g. O/S, DBs, M/W, Network devices and even security software and hardware), sets off a chain of testing, validation, scripting, coordination, and deployment to protect our systems and the global commerce network we support. The burden on our engineering, cyber security and operations team is enormous and continuous. For a company that runs at six nines of availability, even a single patch requires the precision of a surgical procedure. Between the moment a zero day is discovered and the moment a fix can be safely deployed, all enterprises carry real measurable risk.   Here is the truth, the industry has normalized a level of security and quality debt in software/hardware that would be unacceptable in any other safety critical domain.   The digital world depends on a CHAIN-OF-TRUST. Every company, every platform, every experience sits on top of that chain. And today, too many links in that chain are weaker than they should be.   Too many vendors still operate on the same broken logic: Ship first Discover later Patch downstream Make the customer carry the blast radius   That model was already unacceptable.   Now it is becoming DANGEROUS. In a post AI world, an insecure stack is no longer technical debt. It is a PRE-POSTITIONED ATTACK SURFACE. Last week was a water shed moment in this space. A new SOTA (pre-released) model from Anthropic (Mythos) demonstrated the speed with which attackers can discover and weaponize vulnerabilities in critical code bases.   Mythos and more powerful models in the future are not just better at scanning; they are going to compress the cyber timeline. The interval between:   Defect Creation Defect Discovery Exploit Construction Exploit Chaining Exploit Operationalization   Is collapsing.   This is why the vendor wall of shame matters more now than ever before. And vendors that continue shipping preventable insecurity are increasing the amount of machine readable, machine exploitable weaknesses into critical environments. The vendor wall of shame used to be a record of bad engineering. In the post mythos era, it becomes a record of who is unfit for the future.   Security is job 1 at Visa. Security has to be job 1 for ALL providers in the supply chain of technology. And that includes the biggest names in the industry who unfortunately today have some of the weakest records and feature at the very top of my report. This is an urgent call to RAISE THE BAR.

AI security/securing the use of AI is going to kill me. I use Claude Code almost daily. It's a problem.... Here's what I have to change AGAIN this week. Security researcher Ari Marzuk disclosed 30+ vulnerabilities across AI coding tools. Cursor. GitHub Copilot. Windsurf. Claude Code. All of them. He called it IDEsaster. The attack chain includes prompt injection, hijacking LLM context, and auto-approved tool calls executing without permission. Then, legitimate IDE features are weaponized for data exfiltration and RCE. Your .env files. Your API keys. Your source code. Accessible through features you thought were safe. Most studies I read claim that around 85% of developers now use AI coding tools daily. Most have no idea their IDE treats its own features as inherently trusted. 𝗦𝗼... 𝗮𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗿𝗶'𝘀 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗵𝗲𝗿𝗲'𝘀 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗶𝗻𝗴... Be warned: All this is SO much easier said than done! Audit every MCP server connection. Checked for tool poisoning vectors where legitimate tools might parse attacker-controlled input from GitHub PRs or web content. Removed servers I couldn't verify. Disabled auto-approve for file writes. The attack chains weaponize configuration files and project instructions like .claude/settings.json and CLAUDE.md. One malicious write to these files can alter agent behavior or achieve code execution without additional user interaction. Move all credentials to a secrets manager. No .gitignored .env files in agent-accessible directories. API keys live in 1Password CLI. Environment variables inject at runtime through a wrapper script the LLM never sees. Start running Claude Code in isolated containers. Mounted volumes limited to specific project directories. No access to ~/.ssh, ~/.aws, or ~/.config. If the agent gets compromised, blast radius stays contained. Enable all security warnings. Claude Code added explicit warnings for JSON schema exfiltration and settings file modifications. These exist because Anthropic knows the attack surface. Add pre-commit hooks for hidden characters. Prompt injections hide in pasted URLs, READMEs, and file names using invisible Unicode. Flag non-ASCII characters in any file the agent might ingest. The fix isn't to stop using AI coding tools. The fix is to stop trusting them implicitly. What controls do you have for AI tools with write access to your codebase? 👉 Follow for more AI and cybersecurity insights with the occasional rant #AISecurity #DevSecOps

Every two years, we ask thousands of decision-makers around the world one essential question: What are the top risks your organization faces? Since 2007, Aon’s Global Risk Management Survey has offered one of the most comprehensive views of risk anywhere. What began as a list of concerns has evolved into a dynamic signal system — tracking how global volatility shifts and what leaders must do to adapt. This year’s results are revealing. For the first time, geopolitical volatility enters the global Top Ten. Cyber risk remains at the top, but its meaning has expanded. It now reflects the influence of artificial intelligence, supply chain fragility and the pace of digital transformation. Risks once considered operational now impact strategy, brand, capital and talent. Over nearly two decades, we’ve watched business interruption, regulatory change and economic volatility rise, fall and re-emerge in new forms. To illustrate that journey, we created a visual timeline that shows how the risk landscape has evolved — reminding us that resilience isn’t a fixed destination. It’s a capability to be built and continuously redefined. At Aon, we’re in the business of better decisions. That means helping leaders act on what this data reveals: risk is accelerating, complexity is increasing and the ability to navigate both is now a defining advantage. Explore the full report and how it connects to your next decision: https://aon.io/46Q1Xqp   

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

As technology becomes the backbone of modern business, understanding cybersecurity fundamentals has shifted from a specialized skill to a critical competency for all IT professionals. Here’s an overview of the critical areas IT professionals need to master:  Phishing Attacks   - What it is: Deceptive emails designed to trick users into sharing sensitive information or downloading malicious files.   - Why it matters: Phishing accounts for over 90% of cyberattacks globally.   - How to prevent it: Implement email filtering, educate users, and enforce multi-factor authentication (MFA).  Ransomware   - What it is: Malware that encrypts data and demands payment for its release.   - Why it matters: The average ransomware attack costs organizations millions in downtime and recovery.   - How to prevent it: Regular backups, endpoint protection, and a robust incident response plan.  Denial-of-Service (DoS) Attacks   - What it is: Overwhelming systems with traffic to disrupt service availability.   - Why it matters: DoS attacks can cripple mission-critical systems.   - How to prevent it: Use load balancers, rate limiting, and cloud-based mitigation solutions.  Man-in-the-Middle (MitM) Attacks   - What it is: Interception and manipulation of data between two parties.   - Why it matters: These attacks compromise data confidentiality and integrity.   - How to prevent it: Use end-to-end encryption and secure protocols like HTTPS.  SQL Injection   - What it is: Exploitation of database vulnerabilities to gain unauthorized access or manipulate data.   - Why it matters: It’s one of the most common web application vulnerabilities.   - How to prevent it: Validate input and use parameterized queries.  Cross-Site Scripting (XSS)   - What it is: Injection of malicious scripts into web applications to execute on users’ browsers.   - Why it matters: XSS compromises user sessions and data.   - How to prevent it: Sanitize user inputs and use content security policies (CSP).  Zero-Day Exploits   - What it is: Attacks that exploit unknown or unpatched vulnerabilities.   - Why it matters: These attacks are highly targeted and difficult to detect.   - How to prevent it: Regular patching and leveraging threat intelligence tools.  DNS Spoofing   - What it is: Manipulating DNS records to redirect users to malicious sites.   - Why it matters: It compromises user trust and security.   - How to prevent it: Use DNSSEC (Domain Name System Security Extensions) and monitor DNS traffic.  Why Mastering Cybersecurity Matters   - Risk Mitigation: Proactive knowledge minimizes exposure to threats.   - Organizational Resilience: Strong security measures ensure business continuity.   - Stakeholder Trust: Protecting digital assets fosters confidence among customers and partners.  The cybersecurity landscape evolves rapidly. Staying ahead requires regular training, and keeping pace with the latest trends and technologies.  

🗞️ Great 150-page report by Konrad-Adenauer-Stiftung on #AI-Generated #Disinformation in Europe and Africa: Use Cases, Solutions and Transnational Learning, authored by Karen Allen, Africa expert & Christopher N. director of the cyberintelligence institute, Frankfurt 🇩🇪 🔹It analyses how the mechanisms, actors and impacts of AI-driven disinformation evolve across Europe & Africa & proposes strategies for mitigation and transnational cooperation. 🇪🇺- 🌍 Why focus on Europe and Africa? 🔹Because the African dimension is underexplored as most global discourse around AI & disinfo centers on Western democracies. The study aims at bridging a knowledge gap. And the dual-regional focus reflects the globalized nature of disinformation threats. 🤳🏻 Because they are 2️⃣ contrasting digital Ecosystems: Europe a more advanced digital infrastructure, established regulatory frameworks and stronger institutional capacity to respond to disinfo. Africa rapidly digitizing, but still facing infrastructure gaps, less media regulation, and higher vulnerability to foreign influence campaigns due to limited digital literacy and institutional resilience. 🛡️ Because both continents are targets of foreign disinformation campaigns as Russia and China test and adapt tactics across regions. And Africa is sometimes a testing ground for disinformation techniques that are later exported to European contexts. 🗳️ Because elections are common vulnerability & major concern and AI tools are used to manipulate public opinion, polarize societies, and discredit democratic institutions on both continents. 🤝🏻Because the comparative lens helps identify gaps in regulatory and technological responses and Europe and Africa have the opportunity to collaborate more closely through development aid, capacity-building, and cybersecurity partnerships. The report analyses the effects of Generative Artificial Intelligence on the spread of Disinformation : deepfakes, synthetic text, manipulated images.. 🔹It highlights differences and similarities of AI-generated disinformation in both continent 🔹it shows the impact of AI Disinformation on electoral processes as tailored content designed to manipulate voter perceptions and behaviors pose significant threats to electoral integrity. 🔹it looks at the emerging trends across both regions 🔹it identifies the key players behind AI Disinformation involved in disseminating AI-generated disinformation in both geographies 🇷🇺 Chapter 14 focuses on Russia’s use of genAI Disinformation in Europe and Africa, and the following one proposes case Studies of these Russian Actors 🔹Russia is highlighted as a prominent player among all State and non-State actors 👀 Enjoy the read & let’s increase collaboration between nations to develop effective strategies & frameworks to combat the spread of AI-generated disinformation!

In the third part of my Understanding Energy Resilience series, I want to start with something many of you will have seen in the news: recent drone disruptions at major airports. Munich having to temporarily close its airspace. Oslo halting landings. Copenhagen pausing operations for hours. These incidents showed how quickly one small object can halt a critical service, create chaos and cost millions. Now take that thought to energy. If a drone over a runway makes headlines, a drone over energy infrastructure often doesn't. Yet the consequences can be just as real: disruptions to electricity supply, halted rail services and factories forced to stop production. Across Europe, operators are not allowed to neutralize hostile drones themselves – even when a threat is visible above critical infrastructure. Simply put: the rules have not caught up with reality. In my view, clarity and speed here are essential for public safety. Next to physical threats we also face digital ones. Every hour, around 35 million cyberattacks happen worldwide – almost 10,000 every second. Around 5% of them target energy companies and infrastructure. This is the world we operate in: attacks can appear out of nowhere and put entire systems to the test in real time. From my perspective, defending energy infrastructure comes down to a few key priorities: 1️⃣ Let protection happen: Regulation needs to enable energy operators to protect themselves. Clear rules must define who can intervene, when and how – including stopping a hostile drone. We cannot afford hesitation while minutes turn into outages. 2️⃣ Treat physical and digital as one: Fences, cameras and access control on the ground. Network separation and continuous monitoring in the control room. Physical and digital security must be treated as one because if someone can walk in, they can often plug in and disrupt the system. 3️⃣ Harden the infrastructure no one can afford to lose: The majority of physical and cyberattacks on energy systems target a small number of high-impact sites – such as substations, control rooms and interconnectors. Better detection and stronger barriers here make the difference between local disturbance and national outage. 4️⃣ Practice recovery, not just prevention: Real resilience is measured in how quickly power is restored. Simple restart plans, spare parts ready on site and regular drills with operators and authorities turn days in the dark into hours. 5️⃣ Stop naivety – talk openly about risk: We need public awareness without drama – which is one of the reasons I started this series. The more people understand that drones over critical sites are serious and that malware or phishing mails are no joke, the more support there will be for sensible protection. I believe this is the right balance: clear authority to act, practical protection on the ground and in the network with a constant focus on rapid recovery. In a more contested world, that is how energy systems stay open for business.

The Zscaler ThreatLabz research team analyzed a critical remote‑code execution flaw (CVE‑2026‑20131) in the web‑based management interface of Cisco’s Secure Firewall Management Center. The management console didn’t validate incoming data allowing an unauthenticated attacker to send malicious data that the system ran as if it were trusted. This handed adversaries full control to change firewall settings and aggressively move deeper into the network.   This incident is another reminder that exposed management interfaces and legacy assets like VPNs and Firewalls are high‑value targets. Organizations should not only patch but also reduce their attack surface. Moving to a Zero Trust architecture removes public reachability, enforces least‑privilege access and blocks unauthenticated probes. Zscaler’s Zero Trust platform eliminates this risk by creating a one-to-one connection between users directly to applications rather than access to corporate networks, eliminating internet‑exposed endpoints and preventing lateral movement.   Deepen Desai, CSO and EVP of Engineering, and the ThreatLabz team provide a full technical analysis of CVE‑2026‑20131 along with mitigation steps and best practices in their latest blog. https://lnkd.in/gVccQvHi   #ZeroTrustEverywhere #ZeroTrust #ThreatIntelligence #ThreatLabz #CyberThreats