Secure AI & Agentic Development

Building AI Applications, Agents, and Features Securely: Across LLM, RAG, Agentic, and Traditional ML

Every development team is building AI features. Far fewer are building them securely. The pace of AI adoption inside enterprise software has dramatically outrun the pace of AI security expertise. The result is a generation of AI-powered applications and agents being shipped into production with minimal threat modeling, ad hoc prompt-injection defenses, untested output handling, and limited understanding of the new attack surfaces these systems introduce.

The standards and frameworks now exist. The OWASP LLM Top 10 maps the most common LLM application vulnerabilities. The NIST AI Risk Management Framework (AI RMF) and the Generative AI Profile (NIST AI 600-1) provide governance and risk management guidance. MITRE ATLAS catalogs the adversarial threat landscape for AI systems. The OWASP AI Exchange and AI Security Verification Standard are emerging. ISO/IEC 42001 is becoming the management system standard for AI. The frameworks are no longer the gap; rather, implementation is.

Our Secure AI & Agentic Development service helps you bridge that gap. We help organizations securely build LLM-powered applications, agentic AI systems, RAG architectures, and traditional ML deployments.

Our Tailored Approach Can Include

AI Security Strategy &

Framework Alignment

AI security program strategy aligned to NIST AI Risk Management Framework (AI RMF), NIST AI 600-1 Generative AI Profile, ISO/IEC 42001, and applicable sector frameworks.
Threat modeling for AI systems, covering LLM applications, agentic systems, RAG architectures, and traditional ML, using MITRE ATLAS and the OWASP LLM Top 10 as reference frameworks.
Responsible AI (RAI) practices integrated into development workflows: fairness, transparency, accountability, human oversight.
AI security operating model and Security Champions program for AI development teams.
Connection to TBDCyber's broader AI services: AI Governance for the policy and oversight layer, Non-Human & AI Agent Identity for the identity layer, Cloud & AI Workload Security Architecture for the infrastructure layer.
Customized training programs covering AI security fundamentals, prompt injection defense, and AI-aware secure coding for development teams.

Secure LLM Application

& RAG Development

Prompt injection defense: direct injection, indirect injection from retrieved or user-provided content, and jailbreak resistance, including realistic threat modeling rather than blanket "guardrail" promises.
Output handling and sanitization: defending against model-generated content as an attack vector, including XSS via LLM, code injection through generated output, and SSRF/data exfiltration through tool use.
RAG (Retrieval-Augmented Generation) architecture security: source controls, retrieval integrity, prompt construction, and the often-overlooked attack patterns where the RAG corpus itself becomes the attack surface.
Sensitive information disclosure prevention: training data leakage, system prompt extraction, and inference-time data exfiltration through model outputs.
Authentication, authorization, and rate limiting for LLM-powered features.
Model and prompt versioning, testing, and rollback patterns.
Aligned to the full OWASP LLM Top 10

Agentic AI & MCP

Development Security

Secure agentic AI development: scoping, delegated authority, tool-use authorization, time-bounding, and audit trails that tie agent actions back to the originating human or system.
Defense against confused-deputy attacks, where a low-privilege user or system weaponizes a high-privilege agent to take actions they cannot take directly.
Defense against prompt-injection-driven agent misuse: when an agent reads attacker-controlled content (a webpage, a document, an email) and is manipulated into taking actions against its principal.
Agent-to-agent trust patterns and multi-agent system security.Model Context Protocol (MCP) server security: authentication, authorization, scope, audit, and the new attack surfaces MCP introduces as agentic ecosystems mature.
AI gateway and agent orchestration platform security.
Excessive agency limits and human-in-the-loop controls.
Connection to Non-Human & AI Agent Identity for the identity foundation that agentic security depends on

AI Coding Assistant Security
& Traditional ML

AI coding assistant security for development teams: policy, usage guidelines, prompt hygiene, IP protection, secret leakage prevention, and validation patterns for AI-suggested code.
Training data and IP risk management for organizations using AI coding tools at scale.
Traditional machine learning security: adversarial machine learning, model robustness, training data integrity, model supply chain attacks, and model exfiltration.
ML lifecycle security: training data protection, model provenance, deployment hardening, and inference-time monitoring.
Red-teaming AI systems, both LLM applications and traditional ML, to validate that defenses hold under adversarial conditions.
Optional retest and validation engagements as the AI system evolves and new attack patterns emerge

Benefits

Coverage Across the Full Modern AI Development Scope

LLM applications, agentic AI systems, RAG architectures, AI coding assistants, and traditional ML. Most "AI security" engagements address only one or two of these. We can address the full scope of your engineering organization.

Anchored in the Frameworks Buyers and Regulators Now Expect

OWASP LLM Top 10, NIST AI RMF, NIST AI 600-1, MITRE ATLAS, ISO/IEC 42001. The standards conversation is no longer a research topic; it's where AI procurement, audit, and underwriting now live. Your program is mapped to the references that matter.

Connected to a Four-Pillar AI Security Portfolio

Building AI securely (this service), running it securely (Cloud & AI Workload Security Architecture), identifying and authorizing it (Non-Human & AI Agent Identity), and governing its use (AI Governance). One coherent story across four services rather than four siloed engagements.

Vendor-Neutral in a Vendor Landscape That's Loud

The AI security tooling market is fast-moving, fragmented, and prone to overpromising. TBDCyber doesn't resell AI security platforms, so recommendations reflect environmental fit and real capability, not partner-tier commitments.

Resources and Insights

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Related Services

Building AI applications and agents securely is one piece of a broader AI security story.

To run the AI infrastructure securely, see Cloud & AI Workload Security Architecture.

To identify and authorize AI agents in your environment, see Non-Human & AI Agent Identity.

For governance, policy, and Responsible AI oversight of how AI is used across the organization, see AI Governance.

For the broader Secure SDLC program this work integrates into, see Secure SDLC & DevSecOps.

For the methodology service that drives threat modeling for AI systems, see Threat Modeling.

For testing AI-enabled applications, see Application Security Testing.

Your Engineering Organization Is Already Shipping AI. The Question Is Whether It's Shipping AI Securely.

The pace of AI adoption inside enterprise software has outrun the pace of AI security expertise. Prompt injection defense is patchwork. Output handling is ad-hoc. Agentic AI systems are being deployed with delegated authority and minimal threat modeling. AI coding assistants are accelerating velocity and quietly introducing vulnerability at the same time.

TBDCyber helps you ship AI securely, anchored in the recognized frameworks, connected to a coherent four-pillar AI security portfolio across the firm, and informed by direct engagement with the emerging standards. From LLM application development to agentic AI to AI coding assistant security, vendor-neutral throughout.

Talk to a Secure AI Development Expert →