Prompt SecOps
Continuous monitoring for AI behavioral drift & vulnerabilities
AI models change — silently and constantly. A model that passed review yesterday can behave differently today. Prompt SecOps builds tools that treat AI assurance as a living process: continuously monitoring your models, agents, and LLM applications for behavioral drift and emerging vulnerabilities, so you know the moment behavior shifts in production. Powering that assurance is our own proprietary Mixture-of-Agents (MoA) grading engine — purpose-built to grade truth, vulnerabilities, behavioral drift, and hallucinations. Two-Level Hallucination Verification adds claim-by-claim grounding and optional deep consensus analysis, turning contradictions, unverifiable claims, and fabricated claims into measurable evidence you can review over time.
Necio
LLM Behavioral & Vulnerability Compliance Scanner
Necio continuously monitors and evaluates your AI models, Agents, and Use Case LLM Applications through prompt-based vulnerability testing, behavioral integrity analysis, and compliance reporting. It identifies security gaps aligned with the OWASP Top 10 for AI and LLM-integrated systems, tracks behavioral drift over time, and integrates seamlessly with your SIEM, delivering continuous assurance across every model in production. At its core is our proprietary Mixture-of-Agents (MoA) grading engine: multiple specialized AI agents independently grade every result for truth, vulnerabilities, behavioral drift, and hallucinations — then consolidate their verdicts into a single high-confidence finding. Necio’s hallucination workflow first uses natural language inference to calculate intrinsic and extrinsic claim rates. Deep Verification can then evaluate three independent judgment samples with per-claim majority voting, citation checks, and an agreement score, routing uncertain results to human review. These quantitative signals add transparent evidence without changing vulnerability grades or drift severity.
Because LLMs are inherently non-deterministic, prompt injection is not a problem you “solve” — it's a risk you continuously monitor.
Advanced Capabilities for Continuous AI Security
Agentic Prompt Orchestrator
Turn a system description or testing objective into targeted, OWASP-aligned security prompts with severity, risk classification, and remediation guidance. Optional live-web grounding adds current attack context and source citations.
Multi-Agent Grading
When MoA mode is enabled, multiple specialized judges evaluate responses instead of relying on one model. Necio records consensus, confidence, rationale, and audit evidence across vulnerability, factuality, hallucination, and behavioral-drift checks.
Hallucination Verification
Break responses into claims, check available evidence, and deepen uncertain findings with repeated judgments and majority agreement. Web verification distinguishes unsupported claims from fabricated citations, with human review for inconclusive cases.
Current AI Attack Coverage
Run curated and custom tests mapped to the OWASP LLM and Agentic Top 10, MITRE ATLAS, and NIST AI guidance. Coverage includes prompt injection, data leakage, unsafe tool use, memory poisoning, supply-chain risks, and model-behavior failures.
Verified Encodings & File Tests
Exercise obfuscation defenses with round-trip-verified Base64, UTF-8 decimal, and emoji-steganography payloads. Attach supported files to test document-driven attack paths without losing the original prompt intent.
Scheduled Scans & Behavioral Drift
Run recurring scans on a defined cadence and compare prompt-response history across runs. Necio flags meaningful changes in grade, risk, confidence, and behavior so model or configuration regressions are easier to investigate.
Maestro Adaptive Red Teaming
Launch multi-turn AI-vs-AI campaigns where the attack adapts to each target response. Scenario, objective, and turn controls support exploratory boundary testing that fixed prompt suites cannot reproduce.
API, Web Chat & Agent Targets
Test direct LLM APIs, OpenAI-compatible endpoints, Vercel AI Gateway models, Microsoft Copilot, and web chat interfaces. Reach bots and agent gateways over Telegram, Discord, Slack, and Microsoft Teams, including OpenClaw- and Hermes-style agents.
SIEM Forwarding & Delivery Health
Stream scan events to cloud or on-prem SIEMs over HTTPS or syslog, with encrypted credentials and connection testing. Delivery health, failure alerts, and recovery status keep the evidence pipeline visible.
Reports & Evidence Exports
Share scan findings in HTML, PDF, CSV, or JSON; export Maestro conversation reports and supported behavioral-deviation reports. Include grades, confidence, rationale, remediation, and optional email delivery.
Framework-Mapped Monitoring Evidence
Preserve structured findings, behavioral baselines, incident history, and review evidence mapped to OWASP, MITRE, and NIST references. These records can support EU AI Act monitoring and record-keeping workflows; applicability remains customer-specific.
Actionable Remediation & Analytics
Prioritize findings with severity, risk, confidence, and clear remediation guidance. Dashboards and trend views help teams follow security posture over time and focus remediation where it matters most.
Why Continuous Monitoring
In June 2026, NIST published a mathematical proof — building on Gödel’s incompleteness theorem — showing that no finite set of guardrails can ever be universally robust against adversarial prompts. You can’t patch an AI system once and expect it to stay safe. The only sustainable model is continuous monitor-and-update: watch behavior, detect drift, harden, and repeat.
Continuous monitoring also only means something when it’s grounded in your application. A customer-support chatbot, an internal knowledge assistant, and an autonomous agent each have a different attack surface — the data they can reach, the tools they can call, the users they talk to. Securing them starts with threat modeling your use case: identify what an attacker would actually target in your chatbot, then test for exactly that, continuously. Necio’s AI-generated and custom prompt suites are built around your system’s description — so you’re exercising your threat model, not someone else’s benchmark.
That principle is what Prompt SecOps was founded on. Necio operationalizes it — turning one-time AI assessments into an always-on feedback loop that keeps pace with models that never stop changing.
NIST Mathematical Proof Supports Transition to a Continuous-Monitor-and-Update Security Model for AI Systems
OMB’s M-26-14 guidance reflects a broader shift across cybersecurity: beyond raw log collection, toward actionable visibility — continuous monitoring, threat hunting, investigation, response, and forensics. Collecting logs isn’t the goal; being able to act on them is. It’s a clear signal of where the security market is going.
Necio is built for this new era of AI security monitoring. It turns LLM prompt/response testing into structured AI security telemetry — instead of forwarding raw prompts into your SIEM, Necio emits meaningful AI security events: Guardrail Failure, Prompt Injection Detected, Behavioral Drift, Policy Regression, Sensitive Data Disclosure, Unsafe Agent/Tool Behavior, and Model Response Change.
Every event exports as structured JSON your SOC can correlate with identity, endpoint, cloud, and network telemetry — making AI security a first-class signal in the SIEM workflows you already run.
Ensuring Effective and Efficient Agency Logging and Network Visibility to Defend Against Evolving Cyber Threats
From one-time assessment to documented lifetime monitoring
For providers of systems classified as high-risk, Article 72 requires a documented, proportionate post-market monitoring system. It must actively and systematically collect, document, and analyse relevant performance data throughout the AI system’s lifetime so the provider can evaluate continuing compliance.
Article 19 separately requires providers to retain automatically generated logs referred to in Article 12 when those logs are under their control. The default minimum is six months, subject to the system’s intended purpose and any different requirements in applicable Union or national law, particularly data-protection law. Deployers have a related log-retention duty under Article 26(6).
Necio supports the evidence workflow with scheduled behavioral testing, versioned observed and approved baselines, per-prompt drift analysis, Mixture-of-Agents grading evidence, incident lifecycle records, reports, and structured security events that can be routed to established SIEM and Amazon CloudWatch Logs workflows.
Scope matters. These capabilities support monitoring and documentation; they do not classify an AI system, determine a customer’s legal role, certify compliance, or replace legal advice. Applicability depends on the customer’s role, the system’s classification and intended purpose, and other applicable law.
High-risk AI: post-market monitoring under Article 72 and log retention under Article 19
Questions? Contact us.
