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AI and machine learning are redefining how businesses operate powering everything from intelligent customer support to predictive maintenance. But as the use of AI accelerates, so do the security risks lurking beneath the surface. Traditional AppSec tools are not designed for the unique threats faced by AI/ML systems—prompt injection, model tampering, shadow AI usage, and more.
Enter OpsMx Delivery Shield: a purpose-built solution that delivers full-lifecycle AI/ML security. It embeds security checks at every stage of the machine learning pipeline, from data ingestion and model training to deployment and runtime, enabling enterprises to move fast without sacrificing safety.
Let’s unpack the security challenges in AI/ML development, and how OpsMx Delivery Shield is designed to solve them.
The New Threat Landscape in AI/ML Pipelines
Unlike traditional applications, AI/ML systems are vulnerable at multiple levels (refer the image below):
- Data Engineering risks like data poisoning and privacy violations.
- Model Engineering threats, including adversarial attacks, model extraction, and dependency hijacking.
- Deployment-stage vulnerabilities, such as misconfigurations, API abuse, and bias exploitation.
- Runtime threats, especially for LLMs, like prompt injection, hallucinations, and jailbreak attempts.
Moreover, the rise of shadow AI—where teams deploy models without visibility or approval—adds to the governance and compliance burden.
Security teams cannot rely on manual red teaming or code scanners alone. What’s needed is an integrated approach that spans the entire AI pipeline and provides real-time, context-aware insights.
OpsMx Delivery Shield: Built for AI/ML Security
OpsMx Delivery Shield is designed to detect, prevent, and respond to AI-specific threats using open-source tools like NBDefense, Garak, and ModelScan, combined with advanced context graphs and policy engines.
Here’s how it helps secure your AI/ML pipeline end-to-end:
1. AI Discovery & Shadow AI Detection
You can’t protect what you don’t know. Delivery Shield continuously scans environments to identify all AI models, APIs, and tools—including those outside your approved CI/CD pipelines. This visibility is crucial for enforcing governance and eliminating rogue AI deployments.
For AppSec and compliance teams, this capability helps ensure that no model slips through the cracks—closing the loop on unmanaged or unauthorized AI activity.
2. AI Security Posture Management (AISPM)
OpsMx aggregates training metadata, usage logs, lineage, and risk indicators into a unified security graph. This enables teams to understand the real-time risk posture of each AI asset—backed by visual dashboards and scoring.
By prioritizing threats based on exposure and criticality, security teams can focus efforts where they matter most, rather than chasing false alarms or reacting after a breach.
3. AI Red Teaming Support
Delivery Shield supports simulated attacks in staging environments. It can mimic adversarial prompts, run jailbreak attempts, and validate how a model behaves under pressure—before it’s deployed to production.
This proactive approach uncovers unsafe behaviors like hallucinations, sensitive data leakage, or discriminatory outputs, giving teams time to retrain or replace vulnerable models.
4. AI Runtime Defense
Even well-tested models can behave unexpectedly in production. OpsMx protects live inference systems using LLM firewalls and dynamic security rules that react in real time to prompt-based threats, policy violations, or model evasion tactics.
It monitors runtime behavior continuously, flags abnormal outputs, and enforces guardrails automatically—essential for generative AI and autonomous agents.
5. Agentic AI Security
Agentic systems—LLMs that can take autonomous actions or orchestrate APIs—introduce a whole new class of risk. Delivery Shield provides proactive, reactive, and detective controls to safeguard these complex agents.
With real-time remediation and automated guardrails, OpsMx ensures that agentic AI behaves safely and predictably, even as it adapts or learns over time.
6. Context Graph & Policy Engine
OpsMx’s policy engine doesn’t operate in isolation—it uses a context graph that links scanner findings to model versions, training data, code commits, and deployment metadata. This enables policy-driven decisions at every stage of the ML pipeline.
Instead of blanket rules, you get smart policies that consider the full context before promoting a model to production—or rolling it back.
7. Automated Remediation with GenAI
When violations are found, Delivery Shield doesn’t just raise alerts. It suggests specific actions—like rollback, retraining, or isolation—backed by auto-generated remediation playbooks powered by GenAI.
This dramatically reduces manual overhead for AppSec and MLOps teams, while ensuring a fast and coordinated response to threats.
Key Benefits
- Prevents Attacks Before They Escalate – Early detection of vulnerabilities and adversarial threats across the pipeline.
- Delivers End-to-End Protection – Covers ingestion, training, packaging, deployment, and runtime.
- Reduces Manual Overhead – Automates validation, scoring, and enforcement.
- Improves Compliance Readiness – Enforces policies aligned with HIPAA, GDPR, and internal standards.
- Eliminates Shadow AI Risk – Brings all models under centralized governance.
- Accelerates Safe AI Deployment – Enables faster go/no-go decisions using real-time contextual insights.
Conclusion: Don’t Let AI Security Be an Afterthought
AI can create immense business value—but only when deployed securely. With the increasing complexity of ML models, autonomous agents, and regulatory requirements, relying on legacy security tools is no longer enough.
OpsMx Delivery Shield offers a smarter, integrated approach—embedding security throughout your ML pipeline without slowing innovation.
If you’re building or scaling AI/ML in your organization, now is the time to bake security into every stage of delivery.
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