Data443 Risk Mitigation Inc. ($ATDS)

AI tools are getting approved faster than your team can review them. Somewhere in your org right now, a business unit is piloting an agent that touches customer data. The security review is scheduled for next month. Before anything gets a green light, three questions. Non-negotiable. 1. Where does the data go after the prompt is submitted? A bank found out their vendor's model was fine-tuning on submitted prompts. Nobody asked. The contract didn't cover it. That data is out there now. 2. What happens if the AI makes the wrong decision? Wrong answers you can correct. Wrong actions, refunds issued, records changed, emails sent, those already landed. Ask the vendor how they stop a bad action before it executes. Watch how long the pause is. 3. Is there a trustworthy record of every AI decision? When a regulator asks why the agent did what it did, "we have logs" isn't an answer. Logs get edited. You need a record that proves it wasn't. If a vendor can't answer all three, the tool waits. That's the whole system. This is what we built Vaikora for. Runtime enforcement that blocks bad actions before they execute, and a cryptographic audit chain that can't be quietly rewritten. Run the three questions on your next AI approval, and if the answers scare you, talk to us. 👉 Learn more: https://lnkd.in/eCyJ3ntj #AI #EnterpriseAI #AIGovernance #CyberSecurity #DataSecurity #Compliance #RiskManagement #GenAI #Security #Vaikora

We open-sourced a gateway that runs a deterministic policy check before every agent action and writes each decision into a SHA-256 audit chain. The repo ships with compliance presets for SOC 2, HIPAA, GDPR, PCI, and ISO. Each preset defines which actions are permitted, blocked, or held for review at the enforcement point, so you are not writing policy logic from scratch for every deployment. The design keeps enforcement inline with execution. An agent cannot take an action that the policy engine has not already evaluated, which means your audit trail reflects every decision, not just the ones that triggered an alert after the fact. If you are running agentic workflows and need audit coverage that satisfies a real compliance review, the presets give you a starting point your team can extend rather than a blank configuration file. View on GitHub: https://lnkd.in/eTD7FZkS #AIGovernance #SecurityEngineering #AgenticAI #SOC #ComplianceEngineering

🚀 Most security platforms make you go through procurement before you see any value. We took the opposite approach with Vaikora. 🔓 The MCP Data Proxy is open source. Same proxy architecture as the commercial platform. Drop it in front of any MCP-compatible agent and start controlling actions on day one. No signup. No license. No procurement. What you get immediately: ✔ 12+ detection vectors across 4 layers (pattern, semantic, ML, behavioral) ✔ Drop-in OpenAI-compatible proxy (no application rewrite) ✔ Local SHA-256 hash-chained audit log (tamper-evident) ✔ Three modes: • Simulation (Shadow) • Staged • Full Enforcement ✔ Support for 12 LLM providers When you outgrow local → Vaikora platform adds: → Centralized policy across teams and tenants (role-scoped) → Human-in-the-loop approvals (Slack / Teams) → Connectors to: • Microsoft Sentinel • SentinelOne • CrowdStrike Falcon • AWS Security Hub → Compliance presets with auto-generated reports: SOC 2 • HIPAA • GDPR • PCI • ISO 27001 • NIST AI RMF → Up to 99.9% accuracy (controlled eval) → <0.1% false positives in testing AI runtime control is quickly becoming non-negotiable. ⚙️ Start free. Validate in your environment. Then scale. https://lnkd.in/eC955RBF 📅 Book a demo: https://lnkd.in/eetx_SHp

AI agents are already operating inside SOC workflows. They hunt threats. They trigger response. They act fast. But once you give them access - you lose control until after execution. That’s the risk. ⚠️ Example: An agent detects “suspicious” activity → disables accounts Except it was a misconfiguration → 200 legitimate users impacted A prompt injection tricks the agent → data gets exfiltrated No validation. No approval. No control. Data443 Vaikora solves this with pre-execution control 👉 Interceptor proxy between agent and action Nothing executes without validation. How it works: Every action goes through 3 layers: 🧪 Input validation Detects injections, PII, malformed payloads 🧠 Policy evaluation Rules like: “Only disable accounts if inactive 30+ days + approved ticket” ⚡ Enforcement → allow → block → require approval Advanced control modes: • Shadow mode → simulate actions (no risk) • Dry-run → validate without execution • Staged rollout → control production impact Every decision is logged: • full request • policy results • decision + reason • approver (if needed) → secured with SHA-256 hash chains 🔐 What this means: 👨💻 SOC • no more “automation surprises” at 3 AM • visibility into agent decisions 🛠 IT / Security • safe rollout (shadow → staged → live) • policies in plain language (no code) 📊 Leadership • provable controls for auditors (HIPAA, PCI, GDPR) • reduced risk from autonomous agents This doesn’t slow AI down. It makes it safe to scale. Read more: https://lnkd.in/eTkCv5kF Vaikora: https://lnkd.in/ez_R-aRA Integrations: https://lnkd.in/e_Y6E7Ut #AI #AISecurity #CyberSecurity #SOC #SecurityEngineering #AIControl #RealTimeSecurity #DataSecurity #ThreatIntelligence #LLM #Vaikora

🛑 AI agents aren’t just assisting anymore. They’re calling APIs. Writing to databases. Opening tickets. Disabling accounts. Emailing customers. Autonomously. In production. Today. What’s missing? A control layer between what the model decides and what actually happens. That’s Vaikora. ⚡https://lnkd.in/esBzK2c4 A drop-in proxy that intercepts every agent action and makes a decision before execution. What happens in milliseconds: → Input validation Detects PII, prompt injection, jailbreak attempts (12+ detection vectors across 4 layers) → Policy evaluation Deterministic rules + 7-factor risk scoring (0–100) → Enforcement Allow • Block • Redact • Require approval → Full audit trail Every action logged with SHA-256 hash-chain integrity Performance that works inline (not after the fact): ✔ P50 latency: ~8 ms ✔ Block decision: ~18 ms ✔ 10,000+ actions/sec ✔ OpenAI-compatible (12 LLM providers) ✔ Built-in compliance presets: SOC 2 • HIPAA • GDPR • PCI • ISO 27001 • NIST AI RMF ✔ Up to 99.9% accuracy in controlled environments ✔ <0.1% false positives No app rewrite required. Start immediately with: • Shadow mode → observe decisions • Staged rollout → controlled enforcement • Full enforcement → real-time control AI is already executing inside your environment. The question is: who’s approving those actions? 👉 See Vaikora in action: https://lnkd.in/eA8chbWr #AIAgents #AISecurity #LLMSecurity #MCP #RuntimeSecurity #PromptInjection #CyberSecurity #CISO

AI systems are no longer just generating content. They’re executing actions. Calling APIs. Accessing internal systems. Triggering workflows. That’s where a gap is showing up: ▪️activity is logged ▪️alerts are triggered ▪️but actions are not controlled This is already showing up in real environments. That’s why Data443 acquired Vaikora. → https://lnkd.in/e3QePFer Vaikora by Data443 = AI Runtime Control A control layer that evaluates every AI action before execution: 🔴 BLOCK → stop high-risk actions 🟡 FLAG → require review 🟢 ALLOW → safe execution Every action produces a receipt — so teams can prove enforcement, not just activity. Where teams start: 🧩 Open source MCP proxy (fast, drop-in control) → Start Free https://lnkd.in/e3QePFer 🛡️ Enterprise control plane (policy + audit + SIEM integration) → Request 14-Day Pilot: https://lnkd.in/eUviMP5S #AI #Cybersecurity #SOC #ThreatDetection #AIsecurity #DataSecurity #RuntimeControl