AI Securities Blog
AI threats. Model security. Emerging risks.
We track the rapidly evolving landscape of AI security — from adversarial attacks on machine learning models to AI-driven cyber threats, regulatory compliance, and defensive frameworks. Written by security researchers who live at the intersection of AI and information security.
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Recent articles and deep dives
AI Agent Security: Securing Autonomous Agents in Production
Autonomous AI agents are moving from research labs into production environments at speed. Unlike chatbots that respond to single prompts, agents plan, …
State of AI Security: Mid-Year 2026 Assessment
At the midpoint of 2026, the AI security landscape looks dramatically different than it did just twelve months ago. The threats have matured, the …
AI Security in Financial Services: Protecting Algorithmic Systems
Financial services was one of the earliest adopters of AI, and it shows in both the sophistication of AI deployments and the maturity of AI security …
LLM Output Verification: Ensuring Model Outputs Are Safe and Correct
One of the fundamental challenges in deploying LLMs in production is that their outputs cannot be trusted by default. LLMs hallucinate facts, produce …
Healthcare AI Regulation: Security Requirements for Medical AI
Healthcare is one of the most regulated industries for AI deployment — and for good reason. AI systems in healthcare make decisions that affect …
Prompt Injection Defense Evolution: From Filters to Instruction Hierarchy
Prompt injection has evolved from a theoretical curiosity to the most common AI security vulnerability in production. The defenses have evolved too — …
Model Watermarking Techniques: Protecting AI Intellectual Property
Model watermarking has emerged as a critical tool for protecting AI intellectual property. As model extraction attacks become more sophisticated and …
AI Security Conference Season: Key Events and Takeaways
Spring 2026 marks the height of AI security conference season, with a packed calendar of events spanning academic research, industry practice, and …
EU AI Act Compliance: Practical Steps for Security Teams
The EU AI Act’s compliance deadlines are approaching, and organizations deploying AI systems in the European market need to act now. The Act …
Q1 AI Incident Review: Lessons from the First Three Months of 2026
The first quarter of 2026 has been a defining period for AI security. The volume and sophistication of AI-related security incidents has accelerated, …
Training Data Poisoning Prevention: Guarding the Foundation
The foundation of every AI system is its training data. Compromised data means compromised models — and the compromise can be extraordinarily …
Adversarial Patch Detection: Defending Against Physical-World AI Attacks
Adversarial patches represent one of the most practical and dangerous forms of AI attack in the physical world. Unlike digital adversarial …
Government AI Security Mandates: Navigating the New Compliance Landscape
The first quarter of 2026 has seen an unprecedented wave of government actions on AI security. Federal agencies, state legislatures, and international …
AI Red Teaming Frameworks: Structured Adversarial Testing for Models
Red teaming has been a cornerstone of cybersecurity for decades, but AI red teaming requires fundamentally different approaches. Traditional red teams …
RAG Security: Protecting Retrieval-Augmented Generation Pipelines
Retrieval-augmented generation has become the dominant architecture for production LLM applications. By grounding model outputs in retrieved …
Model Extraction Attacks: Protecting Your AI Intellectual Property
Model extraction is one of the most underestimated threats in AI security. An attacker can steal a proprietary model by making enough API queries and …
AI-Powered SOC Tools: Transforming Security Operations
Security operations centers are undergoing a fundamental transformation. AI-powered tools are moving from experimental to essential, changing how …
Deepfake Detection Advances: Keeping Pace with Synthetic Media
Deepfake technology has reached a inflection point. The quality of synthetic audio and video has improved to the extent that traditional detection …
Open-Source AI Model Risks: Navigating a Dangerous Landscape
The democratization of AI through open-source models is one of the most transformative technological shifts of the decade. Anyone can download, …
AI Supply Chain Security: The Hidden Link in Your Model Pipeline
Most AI security discussions focus on the perimeter — protecting API endpoints, filtering inputs, and monitoring outputs. But what if the threat …
Major LLM Vulnerability Disclosures Shake the Industry
The first weeks of 2026 have brought a wave of responsibly disclosed vulnerabilities in popular large language model frameworks and serving …
New AI Regulations Take Effect: What Security Teams Need to Know
January 2026 marks a pivotal moment for AI security. Multiple regulatory frameworks are moving from draft to enforcement, and organizations that …
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Adversarial Patches: When AI Security Gets Physical
We spend a lot of time talking about digital threats to AI. Prompt injection, data poisoning, model extraction – the usual suspects. But what about …