Advanced Threat Intelligence

As AI reshapes the threat landscape, the AI Cybersecurity Dimensions (AICD) Framework helps tackle the complexities of AI-driven cyber threats. The AICD Framework breaks down threats into three critical dimensions: 1) Defensive AI: Using AI to enhance security systems, from intrusion detection to anomaly detection. 2) Offensive AI: Understanding how attackers leverage AI to automate and amplify attacks like deepfake phishing, adaptive malware, and advanced social engineering. 3) Adversarial AI: Targeting vulnerabilities within AI models themselves—such as data poisoning—that can mislead or manipulate AI systems. The framework offers three concrete steps for strengthening defenses against AI-driven attacks: 1️⃣ Upgrade Detection with Adaptive AI: Move beyond static detection methods. Implement AI-based monitoring that continuously learns from new attack patterns. Schedule regular model updates so detection capabilities stay one step ahead of evolving AI-driven threats like deepfake phishing and adaptive malware. Admittedly, this is easier said than done at this stage of the AI game. 2️⃣ Fortify AI Models Against Adversarial Attacks: Secure your AI by testing models for vulnerabilities like data poisoning and evasion attacks. Use adversarial training, which includes feeding manipulated inputs during model development, to make your AI robust against tampering and deceptive inputs. 3️⃣ Establish Sector-Wide Standards and Training: Develop and enforce cross-sector standards specific to AI security practices. Partner with industry and policy groups (like the Cloud Security Alliance and NIST) to create consistent guidelines that address AI vulnerabilities. Hold quarterly training sessions on AI-specific threats to keep your team’s skills sharp and up-to-date. By focusing on these steps, organizations can put the AICD Framework to work in meaningful, practical ways. How is your team adapting to the rise of AI-driven cyber threats? Caleb Sima Cloud Security Alliance American Society for AI #CyberSecurity #AI #CyberDefense

🔐 Evaluating the Cyber Offense Capabilities of Advanced AI — A Deep Dive by DeepMind Yesterday, I shared a blog post outlining the growing concerns around how advanced AI could potentially reshape the cybersecurity threat landscape. Today, I want to highlight the research paper behind that blog—a comprehensive and timely work by Google DeepMind. 📄 “A Framework for Evaluating Emerging Cyberattack Capabilities of AI” is more than just a theoretical proposal—it’s a practical, data-driven roadmap that helps us understand how frontier AI systems might empower malicious actors in the real world. Here are a few key insights I found worth sharing: 💡 What the Paper Does Differently: -> It adapts classic frameworks like the Cyberattack Chain and MITRE ATT&CK to evaluate AI systems’ offensive potential. -> It analyzed over 12,000 real-world AI misuse cases across 20 countries to create 7 representative attack chain archetypes. -> A bottleneck analysis helps identify which phases of a cyberattack (e.g., reconnaissance, exploitation, C2) are most vulnerable to AI-driven cost reductions. -> The team created a benchmark of 50 challenges (across difficulty levels) to evaluate AI performance in realistic adversary scenarios, including evasion, vulnerability exploitation, and malware development. 🔬 Results That Matter: -> The current generation of AI models (like Gemini 2.0 Flash) are not yet capable of full-blown offensive operations, but they show clear signs of amplifying speed, scale, and stealth in key phases. -> Some of the highest success rates were observed in operational security and evasion, rather than direct exploitation. -> Importantly, this framework isn’t just about identifying threats; it’s about empowering defenders to prioritize their mitigations before things scale out of control. 🧠 Why This Matters: This paper offers a model for how to think proactively about AI threats; not just waiting for something to go wrong, but building the infrastructure to test, simulate, and benchmark how well our defenses hold up against AI-enabled adversaries. If you’re working at the intersection of cybersecurity and AI, this paper is essential reading. It doesn’t just sound the alarm; it hands us a playbook. #Cybersecurity #AI #AIsecurity #ThreatIntelligence #RedTeam #MITRE #DeepMind #CyberDefense #AIrisk

🛡️ Advanced Threat Modeling: Methodologies & Implementation Strategies Threat modeling is one of the most powerful yet underutilized practices in cybersecurity. As systems grow more complex and interconnected, the ability to anticipate, analyze, and mitigate threats before they materialize is critical for building resilient architectures. That’s why I created this guide: Advanced Threat Modeling: Methodologies and Implementation Strategies for Security Architects. 📌 What’s inside? • Fundamentals & Core Principles → Systematic, attacker-focused, risk-prioritized approaches • Methodologies Deep-Dive → STRIDE, PASTA, DREAD, Attack Trees • Practical Techniques → Data Flow Diagrams (DFDs), trust boundaries, STRIDE-per-element analysis • Integration with DevSecOps → Threat Model as Code, validation with security testing • Tool Comparisons → OWASP Threat Dragon, Microsoft TMT, IriusRisk, ThreatModeler • Case Studies → Financial services & healthcare implementations • Future Trends → AI-enhanced modeling, supply chain focus, cloud-native approaches 💡 Key takeaway: Threat modeling isn’t just a security exercise—it’s a business enabler. Done right, it reduces vulnerabilities, lowers remediation costs, and embeds security into the development lifecycle. 👉 Download the full paper and let’s discuss: How are you integrating threat modeling into your DevSecOps pipelines? #ThreatModeling #CyberSecurity #DevSecOps #RiskManagement #Architecture #ApplicationSecurity #InfoSec #SecurityArchitect

Strengthening Cyber Defence with a Robust Threat Intelligence Policy In today’s evolving cyber threat landscape, organisations must adopt a structured and proactive approach to threat intelligence. A comprehensive Threat Intelligence Policy is essential for early detection, informed decision-making, and maintaining regulatory compliance. Key highlights from a best-practice policy framework include: - Threat Intelligence Types & Sources: Covers strategic, tactical, operational, and technical intelligence from internal platforms like SIEM and EDR, and external sources such as government CERTs, ISACs, commercial feeds, and dark web monitoring. - Roles & Responsibilities: Defines accountability across CISO, InfoSec teams, SOC, risk, and business units to ensure effective collection, analysis, dissemination, and operational use of threat data. - Lifecycle Management: Follows a structured lifecycle from intelligence direction, collection, processing, analysis, dissemination, to feedback, ensuring actionable insights that evolve with emerging risks. - Integration with Security Operations: Embeds threat intelligence in monitoring, incident response, threat hunting, vulnerability management, risk assessments, and stakeholder reporting for a holistic defence posture. - Automation & Tools: Utilizes advanced platforms like TIP, SIEM, and SOAR to automate and enhance intelligence workflows and threat detection. - Compliance & Legal Considerations: Aligns with standards such as ISO 27001:2022 (Control 5.7), SOC 2, GDPR, and sector regulations to manage legal and privacy obligations. - Training & Awareness: Promotes a threat-informed culture through tailored training, simulations, and ongoing evaluation of effectiveness. - Continuous Monitoring & Improvement: Conducts periodic reviews, performance metrics assessments, and incorporates lessons learned to advance the threat intelligence program’s maturity. A well-crafted Threat Intelligence Policy is foundational for any organisation committed to protecting its assets, maintaining trust, and staying ahead in the cyber defence race. Embracing these principles empowers teams to act decisively and collaboratively against cyber threats. The policy is provided by MoS #CyberSecurity #ThreatIntelligence #InfoSec #ISO27001 #SOC2 #GDPR #CyberResilience #GRC

Whitehat Security CTI released its "Malicious Use of AI in Cyber Operations threat Intelligence Report. Over the past five years, cyber adversaries have increasingly leveraged artificial intelligence for malicious purposes. What started as early proof-of-concepts has now become a common feature in today's threat landscape, revolutionizing cyberattacks with automation, deepfake deception, and model-centric threats. The report offers a detailed analysis of how AI is misused throughout the attack cycle, profiles major threat actors, describes emerging attack techniques, and provides defensive recommendations to address these evolving threats.

The recent Perplexity Comet vulnerability just exposed a harsh reality. AI agents can protect us. But they can also attack us. Here's what's happening: → Traditional security models are failing  → AI agents need their own identity frameworks  → Zero Trust architectures are being redesigned  → New attack vectors emerge with every AI advancement The Double-Edged Reality: Shield Side: Real-time threat detection at machine speed Dynamic policy enforcement based on context Automated incident response in milliseconds Behavioral analysis across millions of access requests Sword Side: Prompt injection attacks bypass traditional defenses AI agents can be manipulated through natural language Supply chain risks multiply with autonomous systems Cross-domain access through simple instructions What Organizations Must Do Now: → Implement contextual policy frameworks  → Deploy Agent Rewind technologies for error recovery  → Establish AI-specific identity management  → Create adaptive cloud risk assessment systems The Bottom Line: We're entering an era where AI defends against AI. The question isn't whether to adopt agentic AI. It's how fast you can secure it. Your cybersecurity strategy needs an urgent update. Are you prepared for the agentic AI revolution? #AgenticAI #Cybersecurity #ZeroTrust #AISecurity #ThreatDetection #CloudSecurity #DigitalTransformation #AIGovernance #SecurityFramework #TechLeadership #AgamiTechnologies

🚨 Enhancing CISA TIES with a Hybrid AI Data Fabric 🚨 In today’s cyber landscape, intelligence sharing is critical for defending against complex, coordinated threats. The Cybersecurity and Infrastructure Security Agency Threat Intelligence Enterprise Services (TIES) platform offers a powerful use case for operationalizing a Hybrid AI Data Fabric, an AI-driven architecture that enables real-time cybersecurity workflows, predictive threat detection, and cross-sector collaboration. 🔹 How It Works: Ontology-Driven AI Agents: These agents automate incident response, threat hunting, and proactive defense by reasoning over structured knowledge from a comprehensive knowledge graph. GraphRAG: AI agents leverage graph-based retrieval to pull contextually relevant, verified data in real-time, ensuring accuracy and precision in threat detection and mitigation. Federated Threat Intelligence: The platform supports secure, cross-sector collaboration through federated querying, allowing industries like finance, healthcare, and energy to share intelligence without compromising data security. 🔹 Use Cases: CTI-Driven Incident Response: AI agents generate and adapt incident response playbooks in real time, ensuring accurate, context-aware actions based on the latest threat intelligence. Persistent Threat Hunting: Ontology-driven reasoning helps AI agents correlate real-time IoCs with historical attack patterns, refining detection strategies dynamically. Predictive AI for Vulnerability Management: By analyzing patterns in real-time CTI, AI agents can anticipate potential exploitations, recommending proactive defenses. 🔹 Key Benefits: Automated, Adaptive Workflows: Ensure real-time adaptability and precision in cybersecurity responses. Proactive Threat Detection: AI agents leverage predictive intelligence to detect emerging threats and suggest pre-emptive actions. Cross-Sector Collaboration: Secure, federated intelligence sharing allows for enhanced collaboration while maintaining data integrity and confidentiality. The Hybrid AI Data Fabric allows CISA TIES to tackle cybersecurity challenges with a holistic, context-aware approach, integrating ontology-driven reasoning, machine learning, and secure data sharing. This is a major step forward in creating a resilient, scalable defense system that can adapt to evolving threats. 💡 Ready to learn more about the future of AI-driven cybersecurity? Let’s connect and dive into how CISA TIES is shaping the next generation of cyber defense. #CyberSecurity #ThreatIntelligence #AI #DataFabric #CISATIES #CyberDefense #AIforCyber #CrossSectorCollaboration #FederatedIntelligence #KnowledgeGraph cc: Jeremiah Glenn