AI in Cybersecurity

The 10 AI Threats Quietly Putting Enterprises at Risk What most companies get wrong about AI security? Thinking it’s just a “tech problem.” It’s not. It’s a behavior problem. Enterprise AI is no longer just answering questions. It’s making decisions. Triggering actions. Accessing sensitive systems. And that changes everything. Here’s the part many teams underestimate: AI doesn’t need to be hacked… It just needs to be misguided. And the impact looks exactly like a breach. Here are 10 AI security threats every enterprise should be thinking about: Prompt Injection Attacks ↳ AI follows malicious instructions → data leaks or wrong actions Data Poisoning ↳ Bad data in training = corrupted outputs at scale Model Inversion ↳ Attackers pull sensitive data from responses Sensitive Data Leakage ↳ Poor context control exposes confidential info API Key & Credential Theft ↳ One stolen key = full system access Unauthorized Tool Invocation ↳ AI triggers actions it shouldn’t even have access to Supply Chain Vulnerabilities ↳ Third-party models can introduce hidden risks Model Drift ↳ AI silently becomes unreliable over time Excessive Autonomy ↳ Agents act beyond boundaries → real-world damage Compliance Violations ↳ AI outputs break regulations without warning What actually protects you isn’t just better models. It’s better control. • Input and output guardrails • Dataset validation pipelines • Access control and tool restrictions • Continuous monitoring • Human-in-the-loop for critical decisions Because here’s the reality: The more powerful your AI becomes… The smaller your margin for error gets. The companies that win with AI won’t be the fastest. They’ll be the most controlled. If you’re deploying AI today Are you treating it like a smart assistant… or like a potential insider with access to everything? Share it with your network. 📌 Follow Marcel Velica for more insights on AI, security, and real-world strategies. If you want short daily thoughts, quick threat observations, and real-time discussions, follow me on X as well →https://x.com/MarcelVelica

📛 CVE 2025 32711 is a turning point Last week, we saw the first confirmed zero click prompt injection breach against a production AI assistant. No malware. No links to click. No user interaction. Just a cleverly crafted email quietly triggering Microsoft 365 Copilot to leak sensitive org data as part of its intended behavior. Here’s how it worked: • The attacker sent a benign-looking email or calendar invite • Copilot ingested it automatically as background context • Hidden inside was markdown-crafted prompt injection • Copilot responded by appending internal data into an external URL owned by the attacker • All of this happened without the user ever opening the email This is CVE 2025 32711 (EchoLeak). Severity 9.3 Let that sink in. The AI assistant did exactly what it was designed to do. It read context, summarized, assisted. But with no guardrails on trust boundaries, it blended attacker inputs with internal memory. This wasn’t a user mistake. It wasn’t a phishing scam. It was a design flaw in the AI data pipeline itself. 🧠 The Novelty What makes this different from prior prompt injection? 1. Zero click. No action by the user. Sitting in the inbox was enough 2. Silent execution. No visible output or alerts. Invisible to the user and the SOC 3. Trusted context abuse. The assistant couldn’t distinguish between hostile inputs and safe memory 4. No sandboxing. Context ingestion, generation, and network response occurred in the same flow This wasn’t just bad prompt filtering. It was the AI behaving correctly in a poorly defined system. 🔐 Implications For CISOs, architects, and Copilot owners - read this twice. → You must assume all inputs are hostile, including passive ones → Enforce strict context segmentation. Copilot shouldn’t ingest emails, chats, docs in the same pass → Treat prompt handling as a security boundary, not just UX → Monitor agent output channels like you would outbound APIs → Require your vendors to disclose what their AI sees and what triggers it 🧭 Final Thought The next wave of breaches won’t look like malware or phishing. They will look like AI tools doing exactly what they were trained to do but in systems that never imagined a threat could come from within a calendar invite. Patch if you must. But fix your AI architecture before the next CVE hits.

Leveraging this new OpenAI real time translator to phish via phone calls in the target’s preferred language in 3…2… So far, AI has been used for believable translations in phishing emails — E.g. my Icelandic customers are seeing a massive increase in phishing in their language in 2024. Before only 350,000 or so people comfortably spoke Icelandic correctly, now AI can do it for the attacker. We’re going to see this real time translation tool increasingly used to speak in the target’s preferred language during phone call based attacks. These tools are easily integrated into the technology we use to spoof caller ID, place calls, and voice clone. Now, in any language. Educate your team & family + friends. Make sure folks know: - AI can voice clone - AI can real time translate to speak in any language - Caller ID is easily spoofed with or without AI tools - AI tools will increase in believability Example AI voice clone/spoof example here: https://lnkd.in/gPMVDBYC Will this AI be used for good? Sure! Real time translations are quite useful for people, businesses, & travel. We still need to educate folks on how AI is currently use to phish people & how real time AI translations will increase scams across (previous) language barriers. *What can we do to protect folks from attackers using AI to trick?* - Educate first: make sure folks around you know it’s possible for attackers to use AI to voice clone, deepfake video and audio (in real time during calls) - Be politely paranoid: encourage your team and community to use 2 methods of communication to verify someone is who they say they are for sensitive actions like sending money, data, access, etc. For example, if you get a phone call from your nephew saying he needs bail money now, contact him a different way before sending money to confirm it’s an authentic request - Passphrase: consider using a passphrase with your loved ones to verify identity in emergencies (e.g. your sister calls you crying saying she needs $1,500 urgently ask her to say the passphrase you agreed upon together or contact with another communication method before sending money)

The world is watching as geopolitical tensions rise, but are we paying enough attention to the hidden battleground of cyberspace? 🤔 In an era of escalating global conflicts, the digital realm has become a theater for new forms of warfare and influence. We see state-sponsored cyberattacks disrupting critical infrastructure, sophisticated espionage campaigns stealing sensitive information, and the weaponization of disinformation to sow discord and manipulate public opinion. The impact of geopolitics on cybersecurity is undeniable, but the questions it raises are complex and far-reaching: How can we balance national security concerns with the need to protect individual liberties and privacy in the digital age? This requires a delicate balancing act, with robust legal frameworks, transparent oversight, and strong encryption technologies playing a crucial role. What role should international cooperation play in establishing norms and deterring cyber aggression? International collaboration is essential. This involves sharing threat intelligence, coordinating responses, and establishing clear rules of engagement in cyberspace. Are we prepared for the potential consequences of a major cyber conflict, and what steps can we take to mitigate those risks? Preparedness involves investing in resilient infrastructure, developing robust incident response plans, and fostering a culture of cybersecurity awareness at all levels of society. The intersection of geopolitics and cybersecurity is a critical issue that demands our attention. I believe that by understanding the evolving threat landscape, promoting international cooperation, and investing in robust cybersecurity measures, we can navigate this complex terrain and build a more secure digital future for all. #cybersecurity #geopolitics #informationwarfare #digitalrisks #cyberconflict

When AI Meets Security: The Blind Spot We Can't Afford Working in this field has revealed a troubling reality: our security practices aren't evolving as fast as our AI capabilities. Many organizations still treat AI security as an extension of traditional cybersecurity—it's not. AI security must protect dynamic, evolving systems that continuously learn and make decisions. This fundamental difference changes everything about our approach. What's particularly concerning is how vulnerable the model development pipeline remains. A single compromised credential can lead to subtle manipulations in training data that produce models which appear functional but contain hidden weaknesses or backdoors. The most effective security strategies I've seen share these characteristics: • They treat model architecture and training pipelines as critical infrastructure deserving specialized protection • They implement adversarial testing regimes that actively try to manipulate model outputs • They maintain comprehensive monitoring of both inputs and inference patterns to detect anomalies The uncomfortable reality is that securing AI systems requires expertise that bridges two traditionally separate domains. Few professionals truly understand both the intricacies of modern machine learning architectures and advanced cybersecurity principles. This security gap represents perhaps the greatest unaddressed risk in enterprise AI deployment today. Has anyone found effective ways to bridge this knowledge gap in their organizations? What training or collaborative approaches have worked?

We all know AI will continue to be the defining conversation for 2026, but what I’m hearing most often from leaders is: “How do we leverage AI without introducing untenable risk?” This year, we will see three defining shifts, all underpinned by the top priority for the CEO and the critical operational mandate for the CIO: security. AI is transforming the threat landscape faster than most organizations can adapt, and a reactive approach is a business risk. An AI-powered defense shield is the foundation for safe reinvention. It’s about real-time visibility, actionable insights, and closing the loop from discovery to remediation across IT, OT, and cloud silos. This strategic and operational imperative shapes our three key shifts: 📌 Proliferation of (Secure) AI Agents: Beyond chatbots to specialized agents embedded in every function - HR, IT, customer service - running autonomous workflows. They become proactive partners, but every connected asset they touch expands the attack surface. The CIO's mandate: ensure this happens securely, at scale. 📌 Deepening Industry Impact with Real-Time Protection: True transformation happens in mission-critical workflows. In healthcare, with thousands of connected devices managing patient data. In manufacturing, on smart factory floors. The CEO needs confidence that business reinvention can happen in their industry; the CIO needs a unified platform to see, decide, and act across it all. 📌 Expanding a Unified Security Posture: Our “ANY” strategy - connecting to any model, any data, any service - demands a unified view of risk. Observability, asset management, incident response… Risk doesn’t stay in silos; to manage it requires architecture that breaks down walls between IT, security, and operations. This is the year intelligent, secure automation becomes inseparable from business strategy. The organizations that thrive will be those that align the CEO's security-first vision with the CIO's execution, proactively seeing every asset, prioritizing every risk, and acting before an incident occurs. Here’s to a transformative - and secure - 2026. #AI #CyberSecurity #DigitalTransformation

Agentic AI Defenders — The Rise of Autonomous Cyber Response For years, cybersecurity has been a race between human endurance and machine speed. Attackers have automated, accelerated, and scaled their operations — while defenders have been left buried in alerts, dashboards, and manual investigation steps. Even with advanced detection tools, the human bottleneck remains the slowest point in cyber defense. The problem isn’t that we can’t see the threats; it’s that we can’t reason through them fast enough. But a new class of AI is changing that equation. Agentic AI — systems that can perceive, plan, and act independently — are emerging as digital teammates within the Security Operations Center. These aren’t just chatbots or automation scripts. They are reasoning agents capable of understanding analyst intent, gathering evidence across domains, forming hypotheses, and autonomously executing containment actions when confidence is high. In short, they don’t wait for instructions — they think ahead. This shift marks the beginning of autonomous cyber response — where AI not only assists but decides. It’s the evolution from static automation to adaptive defense, from data processing to contextual reasoning. And as these AI defenders grow more capable, they’re poised to redefine what “speed” and “precision” mean in cybersecurity operations. Because soon, the most effective analyst in the SOC may not be human at all — it will be agentic. #Cybersecurity #AI #AgenticAI #AIDefense #SOCAutomation #ThreatResponse #FutureOfCyber

A company I know deployed an AI agent in 3 days. No boundaries defined. No guardrails. No sandbox testing. No failure playbook. Week 1: It sent 400 unapproved emails to clients. This is not a horror story. This is what happens when excitement outpaces engineering. The companies succeeding with AI agents in 2026 all follow the same principle: Scaling follows confidence, not excitement. They start small. They define limits. They test adversarial scenarios. They build human approval gates. They observe before they expand. Here’s the step-by-step deployment path serious teams follow - Start with a safe, low-risk use case - Define the agent’s boundaries clearly - Map structured workflows (no guessing) - Ground it with trusted data sources - Apply least-privilege access - Add guardrails before autonomy - Choose the right architecture - Test in simulation (normal + edge cases) - Deploy in a sandbox first - Introduce human approval gates - Add observability and monitoring - Roll out gradually - Create a failure playbook - Build continuous learning loops - Implement governance & compliance controls Safe AI isn’t about slowing down innovation. It’s about engineering trust. Constrain → Ground → Test → Observe → Expand. 15-step framework. Swipe through. Your team needs this before the next sprint planning meeting. What’s the biggest mistake you’ve seen in AI agent deployment? Drop it below 👇

AI is pushing cybersecurity into a phase where human-speed defense is no longer enough.    A recent Communications of the ACM piece (https://deloi.tt/4pKpkcc) by Logan Kugler explores the rise of “counter-AI,” as defenders increasingly turn to AI to keep pace with AI-driven attacks. As automated threats scale at machine speed, detection alone isn’t sufficient.     As Deloitte’s Mark Nicholson shared, many organizations are still applying AI to legacy processes – adding speed without rethinking the operating model. Real progress comes when AI is embedded into workflows, identity visibility, and environments designed for autonomy.    For CISOs, the takeaway is clear: counter-AI isn’t about replacing human judgment, but elevating it. With the right guardrails, AI can absorb the volume and velocity of modern attacks—freeing cyber leaders to focus on resilience, risk, and strategic decision-making in an AI-versus-AI world.