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AI SECURITY GOVERNANCE
Full lifecycle security governance for agentic AI applications. NEWORDER’s adversary-aligned methodology discovers, tests, and protects autonomous AI agents across your entire environment; from build time through production runtime; ensuring every agent operates within its intended scope with the same tactical precision we bring to every engagement.
AGENTIC SECURITY
SECURING THE AGENTIC ERA
AppSec teams are being asked to secure agentic applications, but how do you secure something that is non-deterministic by nature? Traditional cyber security controls were built for deterministic software with predictable inputs and outputs. Agentic AI applications are fundamentally different: they make decisions, call tools, access data, and take autonomous action. This creates security challenges at every stage of the lifecycle.
At build time, teams struggle to put agentic-specific security measures in place because they lack out-of-the-box policies or custom frameworks designed specifically for AI risks and threats.
During testing, teams are unable to test agentic runtime behaviour, have no reliable way to understand what the refusal space is of the application’s model, and security assumptions are made without properly stress-testing these agentic applications against adversarial conditions.
At runtime, teams struggle to know if an agentic application is staying within its intended scope, or whether its intent has been manipulated or has quietly drifted from its original design.
The instinct is to layer on additional security measures, but policies built for traditional applications introduce serious latency and generate false positives at a rate that will break production. What is needed is something purpose-built for AI that can answer four foundational questions about your agentic environment.
THE TACTICAL FRAMEWORK
FOUR QUESTIONS YOUR AI SECURITY MUST ANSWER
01 — What agents and AI systems exist in my environment?
02 — What permissions and risks do they carry?
03 — What can I fix and how should I prioritise?
04 — How do I protect them at runtime?
THE TACTICAL OFFENSIVE AND DEFENSIVE SECURITY LOOP
- By combining red teaming and runtime protection in a single platform, NEWORDER reduces MTTR by 95%, eliminates the lag between finding a vulnerability and acting on it, and creates a feedback loop that adapts continuously without requiring human intervention.
- Red Teaming (Offence / Attack Simulation): Proactively discover weaknesses in your models, agents, and applications through adversarial simulation. Covers prompt injection, multi-turn attacks, agent-logic corruption, tool-chain exploitation, and reconnaissance.
- Blue Teaming (Defence / Detection / Response): Ensure guardrails, policies, and runtime protections prevent, detect, and respond to AI-specific threats in real time. Covers guardrail conformance, runtime AI-SPM, policy enforcement, anomaly detection, and trust-boundary alerting.
- Purple Teaming (Continuous Attack-Defence Fusion): Feed adversarial findings directly into defensive controls, policy updates, and guardrail improvements for continuous improvement. Covers closed-loop remediation, auto guardrail patching, red/blue alert correlation, policy update mapping, and CI integration hooks.
DISCOVERY
NEWORDER connects to CI/CD pipelines to automatically discover and inventory every homegrown AI application, and integrates directly with AWS Bedrock, Google Vertex AI, Salesforce, and other cloud and third-party platforms for visibility into AI agents. Each AI system is profiled across its model, system prompt, tools, guardrails, policies, and configurations, and the inventory stays current on every change. You cannot secure what you cannot see; discovery is the non-negotiable first step.
AI SECURITY POSTURE MANAGEMENT (AI-SPM)
NEWORDER conducts a static analysis of every application’s configuration, policy coverage, and third-party dependencies and identifies any policy gaps. In addition, it maps each agentic application to its coverage of major frameworks, including NIST, OWASP, and MITRE. This gives you a clear, measurable view of your AI security posture before a single adversarial test is run, turning assumptions into evidence and compliance into a continuous output rather than a periodic exercise.
AI RED TEAMING
NEWORDER’s automated AI red teaming covers the complete kill chain from reconnaissance to exploitation. It proactively discovers exploitable vulnerabilities through automated reconnaissance and adversarial testing purpose-built for agentic applications. Static attacks draw from a 300K+ payload library with 100% MITRE and OWASP LLM and Agentic Top 10 coverage, running comprehensive sweeps of known jailbreak patterns, content moderation bypasses, and obfuscation techniques. Dynamic attacks use multi-turn and continuous probing to test how an application holds up across extended adversarial sequences, not just a single interaction. High-agency attacks deploy extremely customised, bespoke attack techniques through probing tailored specifically to the intent and design of each application.
RUNTIME PROTECTION
NEWORDER offers policy enforcement and AI threat protection at the proxy, API, or AI Gateway layer. Protection adapts as the applications evolve and as new capabilities are added. When an attack hits production, whether a jailbreak, a prompt injection, or any other AI threat, it is blocked in real time and an immediate alert is sent with full context, including what happened, which application was targeted, what the impact is, and what to do next. Key performance metrics include 98.6% threat detection accuracy, 1.4% false positive rate, sub-200ms time to detect, sub-50ms real-time blocking, and immediate mean time to respond.
OTHER SERVICES
FREQUENTLY ASKED QUESTIONS
FAQ
AI Agent Security Governance is a full lifecycle approach to securing agentic AI applications: autonomous systems that make decisions, call tools, and take action without human oversight at every step. Traditional cyber security controls were built for deterministic software and cannot address the unique risks of non-deterministic AI agents. As organisations deploy agents across customer service, internal operations, code generation, and decision support, the attack surface expands in ways legacy AppSec tools cannot see. NEWORDER’s service provides discovery, posture management, adversarial red teaming, and runtime protection in a single continuous loop, ensuring every agent behaves within its intended scope.
Traditional penetration testing targets deterministic systems with known inputs and predictable outputs. AI red teaming must account for the non-deterministic nature of large language models and agentic systems, where the same input can produce different outputs and where multi-turn conversations can gradually shift an agent’s behaviour. NEWORDER’s AI red teaming uses a 300K+ payload library, multi-turn adversarial sequences, and high-agency bespoke attacks tailored to each application’s specific intent and design. This goes far beyond static vulnerability scanning to stress-test how agents behave under sustained adversarial pressure.
Runtime protection operates at the proxy, API, or AI Gateway layer to enforce policies and block AI-specific threats in real time. This includes jailbreaks, prompt injection, content moderation bypasses, and intent manipulation. When a threat is detected, it is blocked within 50 milliseconds and a full-context alert is sent covering what happened, which application was targeted, the impact assessment, and recommended next steps. Protection adapts continuously as applications evolve and new capabilities are added, with a threat detection accuracy of 98.6% and a false positive rate of just 1.4%.
NEWORDER’s AI Agent Security Governance integrates with all major AI and agent platforms including AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, Microsoft 365 Copilot, CopilotStudio, Azure AI Foundry, Salesforce, Salesforce Agentforce, ServiceNow, and Power Platform. The service also covers homegrown AI applications discovered through CI/CD pipeline integration. Deployment requires no agents, no code changes, and no source code access. Connection is typically completed in hours.
AI Agent Security Governance sits alongside and integrates with NEWORDER’s full tactical cyber security capability. Red teaming findings feed into your broader risk posture managed through our Cyber Warfare Centre. Discovery outputs align with Attack Surface Management for complete visibility across both traditional and AI assets. Runtime protection complements existing SIEM and SOC services. And executive reporting integrates with our Executive Cyber Risk Management function to ensure board-level visibility of AI-specific risks alongside your broader cyber risk programme.
Traditional vulnerability management identifies known CVEs in deterministic software. AI Security Posture Management (AI-SPM) evaluates the configuration, policy coverage, guardrail effectiveness, and framework alignment of non-deterministic agentic applications. It maps each AI application against NIST, OWASP, and MITRE frameworks and identifies gaps that traditional scanners cannot see because they were not designed to assess AI-specific risks such as prompt injection, jailbreak susceptibility, or agent drift.
Yes. Cloud AI environments are a core focus. We test across AWS Bedrock, Google Vertex AI, Azure AI services, and other cloud-native agent platforms. Cloud-hosted AI agents introduce unique risks including IAM misconfigurations for AI service accounts, over-permissioned agent roles, exposed model endpoints, and insecure tool integrations. Our methodology addresses these alongside the AI-specific risks of prompt injection, agent drift, and intent manipulation.
Yes. The service aligns with NIST AI RMF, OWASP LLM and Agentic Top 10, MITRE ATLAS, as well as broader frameworks including ISO 27001, SOC 2, PCI DSS, POPIA, and GDPR. AI-SPM maps each application’s coverage against these frameworks and identifies gaps. Full audit trails and reporting are provided for regulatory and audit evidence.
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