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Enterprise AI Security Posture Management for regulated teams shipping AI in production.

Penaxtra discovers AI assets, secures LLM agents and MCP servers through a self-hosted runtime gateway, runs adversarial scans, and exports audit-ready evidence mapped to OWASP, NIST, MITRE ATLAS, EU AI Act, ISO/IEC 42001 and ISO/IEC 27001 Annex A.

Dashboard live
Penaxtra console dashboard: posture score 62 of 100 graded C, one open critical finding, mean time to remediate 3.6 days across 13 closed findings, and a 24-hour guardrail traffic chart.
Certified, mapped to, or working toward
ISO/IEC 27001 · certified OWASP LLM Top 10 OWASP Agentic NIST AI 600-1 MITRE ATLAS EU AI Act · Reg. 2024/1689 ISO/IEC 42001 SOC 2 Type II · planned

Penaxtra is an enterprise AI Security Posture Management (AI-SPM) platform that helps regulated teams discover AI assets, run adversarial scans against LLM endpoints and agents, enforce policy through a self-hosted runtime gateway, and export audit-ready evidence mapped to OWASP LLM Top 10, OWASP Agentic Top 10, NIST AI 600-1, MITRE ATLAS, the EU AI Act, ISO/IEC 42001, and ISO/IEC 27001 Annex A.

The platform capabilities in detail

Each pillar sits in a stage of the AI-SPM lifecycle, from asset discovery and adversarial assessment through runtime protection, analysis, compliance mapping and day-to-day operation. Every capability has its own deep-dive page.

10/10
Agentic controls probed

every OWASP Agentic Top 10 control, not just the popular ones.

3,190+
Adversarial probes

across OWASP LLM Top 10 + Agentic.

6
Frameworks

mapped at the control-ID level.

25
Cross-framework overlaps

one finding satisfies multiple audits.

AI-SPM, AI security, and Penaxtra in plain language.

Questions security architects and GRC leads usually ask before the first call. Deeper material lives under /docs and the architecture page.

What is AI Security Posture Management (AI-SPM)?

AI Security Posture Management (AI-SPM) is a continuous-assurance discipline for organisations running LLM applications, agents, MCP servers, RAG pipelines, vector databases, and cloud AI services in production. An AI-SPM platform inventories every AI surface, runs scheduled adversarial scans, enforces runtime policy at the gateway layer, and maps each finding to industry frameworks so security and GRC teams work from the same audit-ready evidence. See the dedicated AI-SPM platform overview.

How is AI-SPM different from Cloud Security Posture Management (CSPM)?

CSPM scores IAM, network, storage, and service configuration in the cloud accounts you operate. AI-SPM extends that with AI-specific control coverage: AI asset auto-discovery, adversarial scanning of prompt flows, runtime gateway DLP and tool allowlisting, and compliance mapping for AI-specific frameworks. CSPM and AI-SPM cover different layers and are used together. The full comparison matrix covers manual pentest, guardrail-only gateways, and compliance spreadsheets as well.

How is AI-SPM different from LLM security?

LLM security focuses on testing and governing large language model applications and prompt flows. AI-SPM is the broader programme that covers asset discovery, runtime controls, compliance evidence, and audit posture across every AI surface. LLM Security Posture Management is a focused layer within AI-SPM - deep-dive on the dedicated LLM-SPM page.

Does Penaxtra map findings to the EU AI Act?

Yes. Every finding is mapped at the article level to the EU AI Act (notably Articles 9, 15, and 17). The same finding row also carries control IDs from NIST AI 600-1, MITRE ATLAS, OWASP LLM Top 10, OWASP Agentic Top 10, ISO/IEC 42001 and ISO/IEC 27001 Annex A, so a single observation feeds the audit evidence pack for every framework. Detail on the compliance page.

How does Penaxtra discover AI assets in our environment?

The platform inventories 11 AI asset kinds today: LLM endpoints, tools and functions, AI applications, vector databases, embedding models, fine-tuned models, self-hosted models, model providers, RAG systems, data sources, and prompt gateways. Discovery is a mix of operator-registered records, tenant-scoped read-only API integrations for managed services, and the runtime gateway reporting back the upstream LLM hosts it sees in traffic. See the platform pillars for the full picture.

Is the runtime gateway on-prem or SaaS?

The runtime gateway is a self-hosted Go agent that runs inside the customer network in front of the LLM endpoint. The control plane is hosted by Penaxtra. Prompt content never leaves the customer network; only allow or block decisions and redacted finding records flow upstream. The agent loads Ed25519-signed rule blobs and refuses anything that fails signature verification. Architecture deep-dive on the architecture page.

Which compliance frameworks does Penaxtra cover?

Seven ship pre-mapped at the control identifier level: OWASP LLM Top 10, OWASP Agentic Top 10, NIST AI 600-1, MITRE ATLAS, EU AI Act, ISO/IEC 42001, and ISO/IEC 27001 Annex A. Twenty-five cross-framework overlap pairs are pre-computed so one finding can satisfy multiple audit requirements without manual re-mapping. Browse the control catalogue.

What does an adversarial scan cost on Penaxtra?

Judging stays efficient through aggressive prompt caching with the judge providers and the Batch API where the scan SLA allows. Scan quotas and endpoint counts are bundled per tier rather than billed by API call. Full plan breakdown on the pricing page.

Can Penaxtra analyze a model from a public registry before we deploy it?

Yes. The Model Card Analyzer takes a public model registry URL or repository id and scores it against more than fifty supply-chain checks before the model reaches production. It flags pickle-format weights that execute code on load, license drift, trust_remote_code custom code, unsafe chat-template tokens, missing safety and bias evaluation, low-adoption uploads, deprecation status, and EU AI Act Annex IV disclosure gaps. Every finding maps to OWASP LLM Top 10, NIST AI 600-1, MITRE ATLAS, and the EU AI Act, and the analysis reads only public metadata - it never downloads or executes the model weights. Background on the supply-chain threat model lives in the MCP tool poisoning write-up.

Are pickle-format model weights a security risk?

Yes. Loading a pickle-format weight file (.bin, .pt, .pkl, .ckpt) executes arbitrary Python defined inside the file, so a tampered or malicious checkpoint runs code on the host that loads it. Safetensors and GGUF are byte-safe alternatives that carry no executable code. Penaxtra flags pickle-only repositories as critical, downgrades when a safetensors alternative ships alongside, and surfaces the registry's own pickle-scanner verdict where the registry publishes one.

Read the full FAQ →

Read what we find before it becomes an incident.

Write-ups on AI security posture, agent and LLM testing, MCP and RAG exposure, and EU AI Act readiness. Written by the team that runs the tests. One email when there is something worth reading, never on a schedule.

Read the architecture deep-dive

Threat model, rule-blob format, gateway deployment guide and a sample scan report.

Open the deep-dive

No email required. The deep-dive is a page, not a gated download.