Adversa AI
Autonomous AI red teaming platform that continuously tests AI agents, LLMs, and GenAI apps. 300+ attack techniques. OWASP & NIST mapped. Trusted b
Custom threat models built around your specific AI stack, covering everything from prompt injection to agentic goal hijacking. Our platform runs autonomous red teaming campaigns on every model update, prompt change, and new tool connection — so your security posture evolves as fast as your AI stack does. Auto generated patches and actionable reports enable your engineers to prioritize fixes, enforce least-agency principles, and verify defenses hold. AI guardrails block known threats — but four attack patterns consistently bypass them. See what AI red teaming finds that guardrails miss, and why both belong in your agentic AI security program. OpenClaw proved high-agency AI works, but banning it won't stop shadow AI or close the competitive gap. Here's the enterprise security strategy you need instead. Adversa AI wins the 2026 BIG Innovation Award for its Agentic AI Security Platform, recognized for advancing continuous Red Teaming for autonomous agents. Discover how the platform helps enterprises address critical risks like goal hijacking and tool misuse, covering the [...] Most AI security assessments focus solely on prompt injection, leaving up to 90% of your agentic AI attack surface exposed. From memory poisoning to tool execution and inter-agent trust, discover the 10 distinct architectural vulnerabilities that could lead to your [...] AI agents don’t just suggest transfers — they execute them. Attackers can now hijack goals, poison memory, and turn your digital workforce against you through natural language manipulation. OWASP’s new framework maps the four pillars of agentic business risk. The [...] As AI systems evolve from passive responders to autonomous agents equipped with planning, memory, and tool use, the Model Context Protocol (MCP) becomes a central architectural layer — and a new security frontier. Yet traditional red teaming approaches are ill-equipped [...] Competition pushes companies to release AI products sooner with no security in mind. Without designing fail-proof AI systems, companies put at risk their businesses, users, and society as a whole. Adversa AI experts are invited to comment attacks on AI, and our research results are published in top-tier media “I would say most of the engineers working on A.I., they don’t understand the new attack vectors,” Alex Polyakov, the founder and CEO of Israeli A.I. security startup Adversa.Al., says. What can we do to minimize the harm from AI? We must understand that we’re creating a new creature that will have great power beyond our own. …if we don’t teach and train it correctly from the very beginning, it can make things worse than they are now. “Research from cybersecurity and safety firm Adversa AI indicates GPTs will leak data about how they were built, including the source documents used to teach them, merely by asking the GPT some questions.” Adversa AI’s technique is designed to fool facial recognition algorithms i
Granica
Compress, sample, scrub, and synthesize. So your models see only the signal, never the noise. Cut Snowflake & Databricks bills by 50%.
For three decades data has behaved like unspent energy: vast, noisy, stubbornly expensive to harness. Analytics and ML engines of today tackle this with brute force, shuffling terabytes through extract, transform, and load pipelines and scanning them in the hope of insight. Granica converts that entropy into intelligence. We weave a reasoning fabric into storage itself so curiosity is never throttled by compute and every table speaks back in real time. We are redefining ETL with E∑L: Extract, Signify, Load. During Signify the system learns while it stores. It compresses exabytes yet retains distributions, keys, and temporal drift, then reasons over a high-dimensional latent space. An analyst can spot a supplier defect before the quarter closes without writing a line of SQL, because the answer is inferred from learned structure rather than mined by a late-night scan. Most replies return without touching cold blocks at all. Granica plucks precise subsets, assembles correlations, or generates counterfactual rows in place, and it falls back to deterministic storage only when confidence dips. Transformation becomes cognition, and warehouses sink into quiet archives instead of standing between a question and its answer. Our first product, Crunch, delivers this leap at the foundation. Drop raw data in and watch storage/compute costs collapse while query latency shrinks from minutes to moments. Analysts can now converse with their tables, auditors follow cryptographic traces to ground truth, and CFOs watch understanding rather than input-output dominate the bill. Compute is no longer paid by the byte but by the residual uncertainty of a question. When understanding outruns batch jobs, the legacy data engines fade and curiosity rises. Imagination becomes the only limit on what data can do. Granica opens that door today.
Adversa AI
Granica
Adversa AI
Granica
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