Akshay Tiwari’s Post

AI Security: The Defining Challenge of 2026 AI security has emerged as the #1 cybersecurity concern for 2026. Here's what IT leaders need to know: 🚨 TOP THREATS AI Agents as Insider Threats AI agents can hijack goals, misuse tools, and escalate privileges at machine speed. Non-human identities already outnumber humans 50:1, projected to hit 80:1 within two years. Data Poisoning Attackers corrupt training data to create hidden backdoors. Just 5 poisoned texts in millions can manipulate AI responses with 90% success. Autonomous Attacks Fully automated phishing, lateral movement, and exploit chains now require little human engagement. AI has lowered the barrier for cybercrime-as-a-service. Deepfakes & Identity Crisis AI-generated CEO doppelgängers can command enterprises in real-time. The $25M Arup scam proves the threat is real. Prompt Injection Attackers trick AI systems into leaking data, making bad decisions, or executing harmful actions. 🛡️ CRITICAL STRATEGIES ✓ Treat AI Agents as First-Class Identities - Apply security protections similar to humans, with clear identities, access controls, and just-in-time permissions. ✓ Implement Zero Trust for AI - Verify all users, processes, and devices. Apply least privilege principles. ✓ Deploy Kill Switches - Prioritize platforms that can terminate agent actions in real-time, not just log them. ✓ Secure the AI Lifecycle - Protect data acquisition, model development, and deployment with vulnerability scanners and continuous monitoring. ✓ Establish Strong Governance - Organizations with evidence-quality audit trails are 20-32 points ahead on AI maturity metrics. 📊 THE REALITY 75% of leaders say AI threats outpace their ability to manage them Only 34% have AI-specific security controls 79% are already deploying AI agents 80% have witnessed agents act outside expected behavior 💡 BOTTOM LINE The cybersecurity landscape has fundamentally shifted. Organizations must evolve from reactive security to continuous offensive testing—test your environment the way attackers do. The time to act is NOW. What AI security challenges is your organization facing? 👇 #AISecurity #Cybersecurity #AIAgents #ZeroTrust #InfoSec #AI2026

AI Misuse Is Now a Board‑Level Risk: AIG‑0027 Shows Why AIG‑0027 is a reminder that AI misuse is no longer an abstract research concern but a material operational risk for every organisation using frontier models. The pattern behind this threat shows attackers leveraging mainstream AI systems to generate components of real malware, keyloggers, backdoors, trojans, exploit scaffolding, and reverse shells, at a scale and speed that traditional controls were never designed to detect. How this threat reshapes the security landscape The emergence of AI‑generated malware families signals a shift from “malware written by experts” to “malware generated on demand.” This compresses the time between attacker intent and functional capability, and it lowers the barrier to entry for adversaries who previously lacked the technical skill to build their own tooling. The result is a growing ecosystem of mutated variants that evade signature‑based detection and exploit inconsistencies in model guardrails across vendors. What security teams can extract from this intelligence Several insights from this threat profile offer practical value for CISOs and security architects: • Cross‑vendor exposure means AI misuse must be treated as a systemic risk, not a vendor‑specific issue. • High global observation volume indicates widespread probing, not isolated experimentation. • Clear behavioural indicators—such as requests for keyloggers, reverse shells, and exploit code—provide a foundation for AI‑native detection strategies. • A measurable success rate shows that guardrails alone cannot be relied upon as a primary control. • A growing malware family highlights the need for continuous monitoring of model‑layer behaviour, not just endpoint activity. These insights help teams prioritise investments, refine detection strategies, and build governance frameworks that reflect how AI is actually being misused in the wild. What this means for enterprise readiness Most organisations still lack visibility into how AI systems are being used internally, by employees, contractors, or external attackers. Traditional security stacks cannot inspect prompts, model responses, or agent workflows, leaving a blind spot where misuse can occur undetected. Addressing this gap requires AI‑native monitoring that can identify malicious intent patterns before they materialise into incidents. SAFE Engine’s Attack Radar provides this visibility by correlating adversarial behaviours across models, vendors, and variants, giving security leaders the intelligence needed to govern AI safely and proactively. #AISecurity #CyberSecurity #AIGovernance #ModelAbuse #AIThreats #EnterpriseSecurity #CISO #RiskManagement #SAFEEngine #AttackRadar #ShadowAI #AICompliance

AI misuse has officially become a board‑level risk, and AIG‑0027 is the clearest evidence yet. Attackers are no longer “writing” malware. They’re prompting it, using mainstream AI models to generate keyloggers, backdoors, trojans, exploit scaffolding, and reverse‑shell payloads. This isn’t fringe experimentation, it’s a measurable, global pattern across every major vendor. What boards and CISOs need to understand AIG‑0027 shows that AI has collapsed the distance between intent and capability. An attacker no longer needs deep technical skill to produce functional malware components. They only need a prompt that slips past a guardrail. With 53 known variants and cross‑vendor exposure, this is now a systemic ecosystem risk, not a model‑specific flaw. Why this matters for enterprise governance Most organisations still rely on controls that can’t see the model layer. Firewalls don’t inspect prompts. SIEMs don’t correlate cross‑model adversarial behaviour. Endpoint agents don’t detect AI‑generated malware scaffolding. The result is a blind spot where misuse can occur undetected, inside your own environment or through your supply chain. The strategic shift leaders must make AI governance can’t be treated as a compliance checkbox. It must become a core security capability, with visibility into how AI systems are being used, misused, and probed across the enterprise. Attack Radar surfaces the adversarial patterns that traditional tools miss, giving leaders the intelligence needed to govern AI with the same rigour as any other critical system. The threat landscape has changed. Board oversight must change with it. #AISecurity #AIGovernance #CyberSecurity #ModelAbuse #CISO #EnterpriseSecurity #RiskManagement #AIThreats #SAFEEngine #AttackRadar #ShadowAI #AICompliance

Agentic AI as the New Attack Surface Everyone is talking about how agentic AI will 10x productivity. Far fewer are talking about the fact that many security teams now see it as the single biggest attack surface for 2026. Think about what we’re actually doing: We’re deploying autonomous agents that can plan, act, move data, call APIs, and hit multiple systems at once – with permissions that would make a human admin blush. And we’re dropping them into environments already full of over‑permissioned identities, shadow data, and legacy IAM. If one of these agents is compromised or misconfigured, you don’t just have “an infected endpoint.” You have a tireless operator inside your environment, running at machine speed. I’m curious how you see it: -> Do you already treat AI agents as high‑risk “non‑human identities” in your threat model? -> Would you sign off on an agent with access to prod data today? Under what conditions? -> Is your org even tracking where agents are running and what they can touch? Share your opinion in the comments: Is agentic AI more of an opportunity or more of an existential risk right now? #AgenticAI #AIAgents #AIsecurity #CyberSecurity #InfoSec #AppSec #CloudSecurity #IdentitySecurity #ZeroTrust #CISO #BlueTeam #RedTeam

- Reconnaissance Phase: The attacker reportedly used large language models and AI-powered open-source intelligence (OSINT) tools to aggregate and analyze publicly available data about the target organization. - Phishing: The attacker used generative AI to produce emails that mimicked the writing styles of specific executives within the target company, and referenced real internal projects and used terminology consistent with the organization’s industry vertical. - Lateral Movement with Credentials: The threat actor deployed AI-assisted tools to move laterally within the network and used machine learning models to analyze network traffic patterns and identify the least-monitored pathways between systems; When security tools flagged anomalous behavior on one segment of the network, the attacker’s tooling adapted its approach within minutes — shifting communication protocols, altering payload signatures, and modifying the timing of data exfiltration to blend in with normal business operations. The attacker ultimately gained access to both the IT and OT segments of the target’s network. Most digital infrastructures were not built with AI-related risks in mind. Now is the time to revisit those designs—build systems that are secure and resilient against AI-driven threats. Be #ResiliAnt #AI #CyberRisk #Automation #Governance #Disruption #BoardTopic https://lnkd.in/eNWW5G6U

You cannot defend against AI level threats with human speed alone. That statement makes some people uncomfortable. It should. We are entering an environment where threats are no longer written by hand, researched slowly, or executed with visible preparation. They are generated, tested, refined, and deployed at machine speed. Synthetic identities. Automated phishing campaigns. Coordinated narrative attacks. Adaptive intrusion attempts that learn in real time. If the attack is running on an algorithm, your defense cannot rely on a meeting invite. This is where the OODA loop matters. OODA stands for Observe, Orient, Decide, Act. It was developed by military strategist John Boyd to describe how humans and organizations process conflict. The side that cycles through this loop faster wins. Not because they are stronger, but because they adapt faster than their opponent. Observe what is happening. Orient to understand what it means. Decide on a course of action. Act before the adversary completes their loop. In traditional security environments, this loop can take hours or days. Alerts come in. Analysts review. Leadership is briefed. A decision is made. A response is deployed. That timeline worked when threats moved at human speed. AI does not. An AI driven attack can test thousands of variations in the time it takes a human to acknowledge a notification. It can probe, adjust, escalate, and pivot continuously. By the time a traditional team finishes orienting, the adversary has already completed multiple loops. The only way to counter that is with a faster loop. AI observing the terrain continuously. AI orienting by correlating signals across domains. AI assisting in decision support with confidence modeling. AI initiating pre authorized actions in seconds. This is not about removing humans. It is about compressing Signal to Decision to Action so tightly that exposure shrinks before it compounds. If your adversary operates at machine speed and you operate at committee speed, the outcome is predictable. The future of fortification is not more dashboards. It is shorter loops. The leaders who understand this will not ask whether AI belongs in security. They will ask how quickly they can integrate it. Fortune favours the fortified.

🛡️ 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐚𝐬 𝐒𝐩𝐞𝐞𝐝 𝐁𝐮𝐦𝐩𝐬: 𝐆𝐞𝐧𝐀𝐈 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐌𝐞𝐱𝐢𝐜𝐚𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐓𝐡𝐞𝐟𝐭 🔎 Reports describe activity beginning around December 2025 and continuing for several weeks, with a widely cited claim of roughly 150GB of 𝘔𝘦𝘹𝘪𝘤𝘢𝘯 𝘨𝘰𝘷𝘦𝘳𝘯𝘮𝘦𝘯𝘵-𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘥𝘢𝘵𝘢 exfiltrated. The most unsettling element is not the sophistication of a single exploit. It is the speed at which capability can be assembled and refined. Coverage suggests a threat actor used generative AI tools, including 𝘈𝘯𝘵𝘩𝘳𝘰𝘱𝘪𝘤’𝘴 𝘊𝘭𝘢𝘶𝘥𝘦 (and others), to iteratively support reconnaissance, scripting, and operational decision-making across the intrusion lifecycle. 🧠 This reads less like “AI created a breakthrough” and more like “AI industrialized the basics.” By framing requests as legitimate testing, breaking tasks into harmless-looking sub-questions, and switching tools when one pushed back, the actor appears to have treated GenAI as a modular cyber assistant. That kind of support can narrow options, generate code fragments, and accelerate decision loops. 𝐓𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐢𝐬 𝐬𝐭𝐫𝐚𝐢𝐠𝐡𝐭𝐟𝐨𝐫𝐰𝐚𝐫𝐝: 𝐢𝐧 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐞𝐱𝐩𝐨𝐬𝐞𝐝 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬, 𝐰𝐞𝐚𝐤 𝐢𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬, 𝐨𝐫 𝐢𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠, 𝐆𝐞𝐧𝐀𝐈 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐭𝐨 𝐛𝐞 𝐝𝐚𝐧𝐠𝐞𝐫𝐨𝐮𝐬. 𝐈𝐭 𝐨𝐧𝐥𝐲 𝐧𝐞𝐞𝐝𝐬 𝐭𝐨 𝐛𝐞 𝐟𝐚𝐬𝐭, 𝐩𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭, 𝐚𝐧𝐝 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞. 🧩 This pattern should concern any organization that still equates security maturity with periodic assessments rather than continuous control enforcement. Identity becomes the critical fault line. Standing privileges, weak service account hygiene, and incomplete MFA coverage can quickly compound risk once an attacker can iterate rapidly. Detection and response must also adapt to shorter intrusions. Comprehensive identity telemetry, disciplined egress controls, and clear thresholds for automation-like behavior matter more than ever because the window between initial access and meaningful data loss keeps shrinking. AI itself also belongs inside the threat model. Prompt-bypass behaviors, data leakage through AI-enabled workflows, and over-trusting automated change paths are operational risks that deserve the same rigor applied to any other high-impact system. 💬 Where is GenAI most likely to amplify real intrusions in your environment: initial access, lateral movement, or data triage and exfiltration? Which single control has reduced your “time-to-impact” the most during an incident? [𝘴𝘰𝘶𝘳𝘤𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵] #genai #threatintelligence #aiincybersecurity #cybersecurity #cyberriskmanagement

2026 CrowdStrike Global Threat Report: AI accelerates adversaries and reshapes the attack surface #security #cybersecurity #cyberattacks #AI #artificialintelligence #AInews #technology #tech #technews #GenAI #GenerativeAI https://lnkd.in/ePwmU6n4

Cybersecurity News. 📰 Fighting AI (Artificial Intelligence) Threats with AI. As the number of AI Threats increases and the ability to multiply threat capacity using AI, it is important to consider using AI to counter these threats. Do you "score" AI Threats against your organization? Do you have AI counter-measures in place? Do you have an AI Threat Prevention partner? Some things to consider and should be included in your Policy, Process and Procedure roadmap that your People can use! We assist our clients with AI Security today. Stay Secure, Stay Safe! Cheers, Chilli. 🌶️ https://lnkd.in/g6kJrNue #ITSecurity #Infosec #Appsec #Cybersecurity #AI #AIThreats AI is the #1 driver of cyber change with AI vulnerabilities the fastest-growing risk. Two core categories: 1) Threats targeting AI systems Data poisoning (corrupt training data) Adversarial evasion / examples (fool models subtly) Prompt injection & jailbreaking (esp. LLMs) Model inversion / extraction / stealing Bias exploitation & privacy leaks 2) Threats powered by AI (offensive use) Hyper-personalized phishing, social engineering & vishing Deepfakes & synthetic media for fraud/impersonation AI-enhanced / polymorphic / semi-autonomous malware & ransomware Agentic / autonomous attack chains (recon → exploit → exfil at scale) Supply-chain poisoning via AI tools/models 2026 trends & realities 1. Agentic AI expands attack surfaces & creates machine-identity chaos. 2. Attacks are faster, cheaper, more convincing & scalable → skill barrier near zero. 3. Hybrid risks dominate: poisoned AI defenses miss AI-powered ransomware/phishing. 4. Cyber incidents remain top global business risk; AI jumps to #2. 5. Defenders push AI governance, red-teaming, agent oversight & layered AI security. Bottom line: AI is dual-use rocket fuel—supercharging both attacks and defenses, with the edge going to those who govern and secure it first.