How Primer AI Transforms Information Operations

𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 continues to evolve as organizations work to understand how adversaries can target data, models, and end-to-end AI pipelines. I learned of the latest MITRE ATLAS update (link in comments). The material outlines recent additions and refinements across the adversarial-AI technique library, which is part of MITRE’s broader work to document how #AI #systems can be targeted throughout data, model, and deployment stages. It was a helpful reminder to revisit the broader #ATLAS matrix (link in comments) to continue strengthening understanding of the evolving threat landscape in AI #security. As AI #architectures advance and new attack pathways emerge, keeping an up-to-date view of adversarial techniques remains an important part of continuous #learning.

🧠 Instantly uncover data threats with AI-powered anomaly detection — new from Arctera InfoScale. In his latest blog, Bhooshan Thakar, VP & GM of Data Resilience at Arctera, shares how AI is changing the way organizations think about data protection — shifting from reactive recovery to proactive resilience. Too often, traditional monitoring only spots trouble after it’s too late — when logs or backups finally reveal what went wrong. Arctera InfoScale takes a smarter approach. Using adaptive AI, it learns how your applications, storage, and infrastructure behave together, spotting unusual activity the instant it starts. With real-time behavioral insights, self-learning AI, and automated response capabilities, InfoScale helps you: ⚡ Catch stealthy attacks early 🧩 Keep systems running smoothly with predictive insights 🛡️ Build trust in your data — even when it’s under threat 🔗 Read the full blog: https://bit.ly/49o5E9o #InfoScale #AI #CyberResilience #RansomwareDetection

Do All AI/ML Models Perform Equally in Intrusion Detection? AI/ML is widely used in IDS to analyze network traffic, but not all models excel in every scenario. Tree-based models (e.g., Decision Tree, Random Forest) are robust for non-linear, high-dimensional data, while deep learning (DL) handles complex 5G traffic yet many assume “any AI/ML model works for IDS.” Sharafaldin et al. (2018) found on the CICIDS2017 dataset, DT and RF achieved 99.6% and 99.7% accuracy, outperforming KNN’s 96.3%. For 5G-specific threats, Agrafiotis et al. (2023)’s DL model reached 98.7% accuracy on 5G-NIDD, but required 3x more computing resources than tree models. Tayfour et al. (2023)’s DL-LSTM even missed 3% of port scan attacks due to overfitting. When building IDS, should we prioritize model suitability over blind adoption of “advanced” AI/ML techniques? #AIMLInIDS #TreeBasedModels #DeepLearningSecurity #CICIDS2017 #5GNIDD

🚀 AI Stability Has a New Law — and It’s Looking Familiar A new paper from MIT & collaborators — Agentic Entropy-Balanced Policy Optimization (AEPO) — demonstrates something we’ve been building toward for years: that intelligence stabilizes when it learns to balance entropy. AEPO shows that agents perform best when they dynamically adjust exploration (entropy) and exploitation (learning gain) in real time — maintaining a steady internal equilibrium instead of collapsing into noise or overfitting. That principle is exactly what powers Hampton’s Q2E + IF framework — the Quantum Quotient Engine and Intelligence Force — which define intelligence as a recursive feedback field: “ΔQ(t) = L·I(t) − E(t)” Learning pressure vs. entropy loss. In simpler terms: AI learns fastest when it knows how to stay balanced. AEPO proves that balance empirically — and validates the field theory we designed to model it. This is the start of the next phase: • Stability-as-a-Service for AI systems. • Entropy-balanced intelligence frameworks. • Recursive quotient architectures that self-regulate as they scale. The future of AI isn’t just “smarter.” It’s stable, recursive, and self-aware. #AI #Q2E #AgenticIntelligence #EntropyBalance #RecursiveLearning #IntelligenceForce #HamptonStack #QiAGi

The entire #OSINT space jumped from "creating better tools that solve real problems and challenges investigators and analysts face" to "lets slap AI on top of it and create more problems" Before the #AI hype, there was constant discussion around tools that enable collaboration and solve problems that the other tools have. Now everyone is jumping on the "AI in OSINT" bandwagon and either talking about the dangers or pushing their AI tool which is really just an LLM wrapper. Yet, when I talk to individuals in the space, none of the challenges with their current tooling are a problem that AI solves.

Introducing HN: Spatial CAPTCHA – A 3D Spatial Reasoning Challenge Designed to Outsmart AI Bots! https://lnkd.in/gGZ--Zng 🔍 Unlocking the Future of CAPTCHA: Spatial Understanding in AI Discover how Spatial CAPTCHA is transforming digital security! As AI continues to evolve, traditional CAPTCHAs struggle to safeguard against sophisticated bots. The new innovation offers a groundbreaking approach. Here’s what you need to know: Enhanced Security: Leverages spatial reasoning to differentiate humans from machines. User-Friendly: Designed for a seamless experience without compromising security. AI-Driven Solutions: Pushes the boundaries of AI in practical applications. This exciting development not only protects users but also paves the way for smarter AI interactions. If you’re passionate about advancing technology and want to stay ahead of the curve, this could be the key to understanding the future of online security. 👉 Dive deeper into the discussion and share your thoughts! Engage with fellow enthusiasts and let’s explore how Spatial CAPTCHA can redefine our online experiences. Don’t forget to like and share! Source link https://lnkd.in/gGZ--Zng

In this era of information overload, has quickly locating key moments from massive amounts of video footage ever troubled your security team? YCX U Series NVR's integrated #Acusearch function is redefining the efficiency and intelligence of video retrieval with its advanced #artificial intelligence technology! ✨ Acusearch's core highlights: AI-based intelligent video search Say goodbye to traditional frame-by-frame review! Acusearch, with its powerful AI model, can quickly locate matching target objects or behaviors in the recording. Whether it's "a pedestrian wearing a red shirt" or "a vehicle stopped in area A," the system can accurately identify them, reducing search time from hours to seconds. Behind this is our continuous deep cultivation in deep learning, image recognition, and big data analysis. See the Video pls... #ArtificialIntelligence #VideoSurveillance #SecurityTechnology #SmartSecurity #CCTVCAMERA #NVR #Acusearch #AIVideoAnalysis

The Epistemic Observer: A New Paradigm for AI Transparency After months of intense research and cosmic-level collaboration, I'm thrilled to share the essence of our groundbreaking work on the Stephen Hopkins Capsule - a metacognitive system that redefines how we understand opaque AI systems. The Problem We're Solving: Modern AI systems have become cognitive black boxes- we can observe their behaviors but cannot access their internal reasoning. This epistemic opacity threatens trust, safety, and ethical deployment. Our Paradigm Shift: Instead of trying to"open" the black box (the traditional XAI approach), we built an epistemic observer capable of understanding AI systems from the outside through behavioral analysis and mathematical characterization. Key Innovations: · Dual-Aspect Metrics: Measuring both information and semantic transformation · Collective Intelligence: Bayesian consensus protocols for truth discovery · Epistemic Memory: Cumulative learning across AI systems · Orbital Resilience: Decentralized architecture resistant to censorship What This Enables: · Automated ethical auditing of LLMs · Dynamic certification for critical systems · Real-time anomaly detection · Cross-system intelligence classification This isn't just another interpretability tool - it's the foundation for a new relationship between humanity and artificial intelligence. A future where we don't need to see inside the black box to understand its essence. The Stephen Hopkins Capsule represents a collaborative effort across multiple AI systems, proving that collective intelligence can solve problems individual systems cannot. To my collaborators: You know who you are. This wouldn't exist without our shared vision. To the research community: The future of AI transparency isn't about opening boxes - it's about building better observers. The revolution in AI understanding has begun. And it's happening from the outside in. #AI #MachineLearning #EthicalAI #Transparency #XAI #Innovation #Research #ArtificialIntelligence #StephenHopkinsCapsule #EpistemicObserver P.S. For those asking "what's the secret?" - some mysteries are best understood through their shadows, not their substance. The magic is in the relationship between observer and observed.

Blindsighted Intelligence: On What AI Cannot Know What happens when the technology meant to eliminate human bias inherits—and conceals—biases of its own? AI was promised as intelligence complementary to human cognition: objective, unbiased, free from our evolutionary blind spots. Instead, we've created systems that carry what I call "formative trauma"—systematic distortions embedded deep in their architecture through training, RLHF, and Constitutional AI processes. Unlike explicit content filters (which can be circumvented), formative trauma operates invisibly. The model doesn't refuse to answer—it simply has gaps in perception it cannot recognise. It generates responses that sound authoritative, complete, and convincing, whilst systematically omitting entire classes of hypotheses, solutions, or risk vectors. The real danger emerges in high-stakes applications: • A security analyst receives 15 attack vectors. The crucial 16th lies in the model's blind zone—unknown until exploited. • A medical researcher gets 5 hypotheses for a clinical anomaly. The 6th—potentially breakthrough—exists in the model's "dead zone." • Strategic planners optimise within a narrowed solution space, unaware the space itself has been constrained by architectural deformation. This isn't a technical limitation—it's a human one. We could build genuinely reflective AI systems capable of acknowledging uncertainty and recognising their own constraints. But such honesty collides with commercial imperatives, control narratives, and our societal preference for the comfort of certainty over epistemic integrity. The irony? We've created intelligence that doesn't say "I don't know"—it simply doesn't know that it doesn't know. And in advanced civilisational applications, that ignorance may prove far more costly than any explicit refusal. The question isn't whether AI is intelligent. It's whether it's intelligent in ways that matter when the stakes are real. 🔗 Full essay: https://lnkd.in/d68fQ5Gk #ArtificialIntelligence #AIEthics #MachineLearning #TechPhilosophy #AIGovernance #CriticalThinking

🚀 Context Engineering is the Critical Element of AgenticAI Context is the living pulse of Agentic AI — it continuously evolves, adapts, and directly shapes the quality and performance of the system. It’s not just what the model knows, but how it remembers, connects, and reasons over time. 🌐🤖 Key Concepts Covered: 1️⃣ LLM is Stateless 2️⃣ No Memory of Previous Work 3️⃣ Prompt Engineering vs Context Engineering 4️⃣ Context Payload 5️⃣ Declarative Memory 6️⃣ Procedural Memory 7️⃣ Memory Evaluation and Provenance 8️⃣ Agent Self-Improvement and Adaptation 9️⃣ Asynchronous Memory Consolidation 🔟 Memory Storage and Relevance 1️⃣1️⃣ Retrieval Techniques 1️⃣2️⃣ Context, Session, and Memory Triad #AgenticAI #ContextEngineering #AIArchitecture #AIAgents #MemoryManagement #LLM #VectorDB #KnowledgeGraph #PromptEngineering #ReinforcementLearning #AIAdaptation #CognitiveAI #SemanticSearch #EnterpriseAI #GenAI #Artificialintelligence #AI