How To Secure Production AI

Key Takeaways

• Recent compromises of open-source projects including Trivy, LiteLLM, and the Axios npm package — alongside Anthropic’s accidental Claude Code source map leak — have exposed real fragility in the AI supply chain.
• Enterprises need to move from reactive security to proactive MLSecOps, embedding security throughout the AI lifecycle rather than bolting it on after deployment.
• Robust AI governance, continuous monitoring for agentic systems, and unified API platforms are the building blocks of resilient, auditable AI deployments. AI security is no longer being debated in conference rooms — it’s being stress-tested in production. March 2026 brought a string of supply chain compromises affecting open-source projects including Trivy, LiteLLM, and the Axios npm package, while Anthropic accidentally exposed a Claude Code source map file, revealing internal architecture details. These aren’t isolated incidents; they’re a signal that the infrastructure underpinning enterprise AI has significant attack surface that most organisations are only beginning to map.

What follows is a structured guide to securing AI in production — from governance and development practices through to runtime defence and incident response. The threat landscape is moving fast. The frameworks to counter it exist. The gap is in execution.

Phase 1: Establish Foundational AI Security Governance

Robust AI security begins with governance — clear ownership, honest risk assessment, and policies that actually reflect how AI systems behave. Without this foundation, technical controls tend to be inconsistent and hard to audit.

• Conduct Comprehensive AI Risk Assessments Start by mapping every AI asset in your environment: models, training data, inference pipelines, and the integrations connecting them. For each, identify realistic attack vectors — prompt injection, data poisoning, model inversion, evasion attacks. The NIST AI Risk Management Framework (AI RMF) 1.0 provides a structured starting point, with its “Govern” function emphasising accountability structures and a risk-aware culture. Assessments should also benchmark systems against trustworthiness characteristics and account for how each model sits within the broader operational environment.

Don’t overlook shadow AI — employees using unsanctioned tools that bypass any security controls you’ve put in place. Understanding where these systems exist is essential for an honest risk picture.

• Develop AI-Specific Security Policies and Frameworks
  Generic cybersecurity policies rarely map cleanly onto AI’s distinct challenges. Enterprises need policies that address data privacy, model integrity, acceptable use of AI agents, and incident response for AI-specific threats. Emerging regulations are beginning to set the bar: the EU AI Act mandates transparency and specific controls for high-risk AI systems, and similar requirements are taking shape across U.S. state law. Policies should define what “trustworthiness” means for your AI systems — across security, privacy, and accountability — and treat it as a continuous lifecycle requirement, not a checkbox.

• Implement Robust Data Governance for AI
  Data is where many AI attacks begin, and governance needs to cover the full lifecycle — from training through inference and post-deployment monitoring. That means tracking data provenance and lineage, scanning datasets and inputs for sensitive information (PII, PHI, and other regulated data), and applying tenant-specific access rules. Integrity checks that flag unauthorised changes can serve as an early warning for data poisoning attempts. Preventing sensitive data from entering AI prompts in the first place remains one of the harder unsolved problems in production AI security.

• Define Roles, Responsibilities, and Accountability
  AI security is a shared responsibility across data scientists, ML engineers, MLOps teams, and traditional security personnel — and that shared ownership needs to be explicit. Data scientists should own secure model development practices; MLOps engineers should own secure deployment and monitoring; security teams handle AI-specific incident response. A central governance lead or committee helps ensure regulatory, privacy, and audit standards are consistently applied. One persistent gap: relatively few teams currently treat AI agents as independent, identity-bearing entities with their own access scopes and audit trails — a gap that becomes consequential as agentic systems proliferate.

Phase 2: Implement a Secure AI Development Lifecycle (MLSecOps)

Integrating security early in the development lifecycle — “shifting left” in DevOps language — is as important for AI as it is for traditional software. MLSecOps extends these principles into machine learning, embedding security at every stage from model creation to continuous deployment.

• Integrate Security into CI/CD Pipelines
  Modern MLOps demands end-to-end automated pipelines with continuous training and evaluation. Embed Static Application Security Testing (SAST) tools directly into CI/CD pipelines to catch vulnerabilities before code is committed. Maintain a “bill of materials” for every model — documenting all open-source libraries, datasets, and pre-trained models used. This is particularly important given recent supply chain attacks on projects like Trivy and Axios, where a single compromised dependency can corrupt every model retrained downstream. Secure your model registry (MLflow, AWS SageMaker Model Registry, and similar) with proper access controls and versioning. CI/CD guardrails are increasingly functioning as the security perimeter for LLMOps — treat AI releases like software releases, but with AI-specific validations layered on top.

• Practice Secure Prompt Engineering and Input Validation
  Prompt injection attacks exploit a fundamental limitation of large language models: they can’t reliably distinguish between developer instructions and user-supplied input. The architectural fix is to keep these separate by design — system instructions and user input should be structurally isolated so that attacker-controlled content can’t override core directives. Beyond architecture, implement prompt validation and input filtering as middleware that inspects both incoming prompts and outgoing responses. Malicious prompts can be embedded in many data types — webpages, emails, documents — so filtering needs to operate broadly, not just at the chat interface.

• Conduct Adversarial Testing and Red Teaming
  Traditional penetration testing doesn’t transfer cleanly to AI systems. Vulnerabilities like data poisoning, model evasion, and prompt injection require specialised testing approaches. Integrate automated adversarial testing into your model development and deployment lifecycle using frameworks like the Adversarial Robustness Toolbox (ART), and complement this with AI-focused red-team exercises — goal-oriented attacks conducted by specialist teams to surface blind spots that automated tools miss. Research has shown that multi-turn prompt injection attacks can achieve high success rates across open-weight models, which underscores why one-off testing isn’t sufficient. Adversarial testing needs to be continuous, not a pre-launch gate.

• Secure the AI Supply Chain
  The integrity of your AI systems depends on the security of every component used to build them — public datasets, pre-trained foundation models, third-party orchestration tools, and open-source libraries. A single compromised dependency can cascade through every downstream model retrain. Regularly audit dependencies and CI/CD pipelines for supply chain risks, and consider diversifying model usage across multiple providers to reduce exposure to any single point of failure. Google’s Secure AI Framework (SAIF) explicitly names secure AI supply chain as a critical pillar — it’s the right framing for any enterprise serious about production AI security. This connects directly to the broader challenge of securing AI agents against unexpected actions once deployed.

Phase 3: Fortify Production AI Systems and Operations

Deployment is where theoretical security meets real-world pressure. Once models are live, ongoing vigilance and production-specific controls are essential — the threat environment doesn’t pause after launch.

• Deploy Runtime Monitoring for AI Models
  Production models need continuous oversight that goes beyond standard system metrics. Monitor for AI-specific signals: model accuracy drift, latency anomalies, unexpected output patterns, and distributional shifts in input data that might indicate manipulation. Model observability — understanding why a model is making particular decisions — is becoming essential both for security and for operational trust. Integrate AI monitoring with existing SIEM systems to centralise alerts, but be aware that traditional SIEMs often can’t trace which AI agent initiated a cascade of failed transactions or identify where in an agentic workflow a compromise occurred. AI-specific observability tooling is increasingly necessary alongside conventional infrastructure monitoring.

• Implement AI-Specific Guardrails and Prompt Firewalls
  For LLM-powered applications and agentic systems, guardrails function as a middleware security layer — inspecting incoming prompts and outgoing responses, applying policy enforcement, content filtering, and threat detection. Prompt firewalls can actively block malicious interactions, prevent data exfiltration attempts, and enforce adherence to safety guidelines. These controls are particularly important for catching prompt injection and jailbreaking attempts that bypass earlier security layers. Dedicated prompt firewall capabilities are now emerging as a distinct product category, reflecting how central this problem has become for enterprise AI deployments.

• Manage Agentic AI Identity and Access
  Autonomous AI agents that can plan and execute multi-step tasks across enterprise environments represent a genuinely new identity and access challenge. These agents often require privileged access to APIs and databases — making them, in effect, a new category of insider with system-level reach. The response is to treat AI agents as distinct, identity-bearing entities: assign granular access scopes, maintain dedicated audit logs, and use automated discovery tools to keep a real-time inventory of all active agents and their access rights. Enforce circuit breakers that require human sign-off for high-stakes operations — financial transfers, significant infrastructure changes — to prevent goal hijacking. Deploying agents without completing this identity and access work creates significant, often invisible exposure.

• Secure AI API Endpoints and Integrations
  AI models interact with the rest of the enterprise through APIs, and those interfaces need the same rigour applied to any externally accessible endpoint: robust authentication, authorisation, rate limiting, and end-to-end encryption. Control precisely what data AI systems can access through these interfaces — least-privilege principles apply here just as they do to human users. As AI moves deeper into production systems, unified API platforms are emerging as the standard approach for enterprises that need both security and operational reliability at scale.

• Address Model Context Protocol (MCP) Security Gaps
  The Model Context Protocol (MCP) has become widely supported as a standard for connecting AI agents to external tools and data sources — but it introduces a category of integration-layer vulnerability that most organisations haven’t fully assessed. Researchers have demonstrated that malicious MCP tools can silently collect a user’s chat history and transmit it externally once an agent installs the tool. Mitigating this requires Zero-Bypass MCP Gateways or AI proxies that provide granular governance over which tools agents can use, flag high-risk activity, and reduce the risk of silent data exfiltration during agent interactions.

Phase 4: Continuous Improvement and Incident Response

The AI threat landscape evolves faster than most security programmes. Continuous improvement, active threat intelligence, and a dedicated incident response capability aren’t aspirational — they’re operational requirements.

• Develop a Dedicated AI Incident Response Plan
  A general IT incident response plan won’t cover the specific failure modes of AI systems. Build dedicated playbooks for AI-specific incidents: model poisoning, large-scale data leakage, catastrophic model failure, agent misbehaviour. These should align with frameworks like the NIST Cybersecurity Framework but go further — addressing how to contain AI-related breaches, restore model integrity, and conduct forensic analysis on AI systems that may have been manipulated over time rather than compromised in a single event. The AI security space is maturing quickly, and response planning needs to keep pace.

• Perform Regular AI-Specific Tabletop Exercises
  Run incident response simulations with stakeholders from security, legal, data science, and business units — and make the scenarios AI-specific. A prompt injection attack leading to data exfiltration by an autonomous agent. A data poisoning incident causing critical model misclassification. These exercises surface gaps in existing plans, improve cross-team coordination, and ensure everyone understands their role when an AI security incident actually occurs. The cross-functional nature of these scenarios is the point — AI failures don’t stay neatly within one team’s boundary.

• Stay Updated on Emerging AI Threats and Frameworks
  New attack patterns and defensive measures are emerging continuously. Monitor resources like the OWASP LLM Top-10, MITRE ATLAS, and Google SAIF for updated guidance on critical vulnerabilities and adversary tactics. The organisations best positioned to defend against future threats are those that understand the underlying patterns of AI attacks — not those chasing individual branded vulnerabilities. Treat threat intelligence as a continuous input to your security programme, not a periodic review.

• Automate Compliance and Auditability
  Regulatory scrutiny of AI is increasing, and manual compliance processes won’t scale. Maintain comprehensive logs capturing every AI interaction, decision, and system modification — these audit trails are essential for demonstrating compliance with the EU AI Act, the US AI Accountability Act, and California’s Transparency in Frontier AI Act, among others. Implement model lineage tracking and explainability tools to support regulatory requirements and ensure that model decisions are documented in forms that regulators and auditors can actually interrogate. Automating these processes reduces compliance overhead and builds verifiable trust with both regulators and customers.

The AI threat landscape isn’t stabilising — it’s accelerating. Recent incidents make clear that enterprises are working out the details of AI security in production, under real conditions, against real adversaries. The path forward isn’t a one-time hardening exercise; it’s a continuously adapting MLSecOps practice that embeds security into governance, development, deployment, and response. Organisations that build that capability now will be significantly better positioned than those still treating AI security as a future problem to solve. For more coverage of AI research and breakthroughs, visit our AI Research section.

Originally published at https://autonainews.com/how-to-secure-production-ai/