AI Security Architecture for Production Systems Under Active Threat
Adversarial hardening, supply chain integrity, and sovereign deployment architecture for organizations running AI in production.
Solutions for AI Security & Resilience
AI Supply Chain Security & Model Integrity
AI supply chain security consulting. We build model vetting pipelines, ML-BOM architecture, and shadow AI governance for CISOs at regulated enterprises. NIST AI 100-2 and EU AI Act compliant.
Biometric & Facial Recognition Compliance
Whether you have deployed facial recognition and need to know your exposure, or you are evaluating vendors and want to get it right the first time, we audit biometric systems against the regulations, benchmarks, and operational standards that actually matter.
Software Update Deployment Integrity & IT Resilience
On July 19, 2024, a single configuration file crashed 8. 5 million Windows machines in under 90 minutes. Not malware.
Sovereign AI & Private LLM Deployment
One in five organizations has already suffered a breach from unsanctioned AI tool usage. Banning AI does not work. Building secure, sovereign alternatives does.
Related AI Services
Frequently Asked Questions
Should we hire an AI security consultancy or build an internal AI security team?
The honest answer is you need elements of both, and the timing matters. Building an internal AI security team from scratch takes 12-18 months to hire, train, and operationalize. The talent pool is thin: offensive AI security researchers who can red-team production LLM systems and then architect the fixes are not abundant. A consultancy gets you to a defensible security posture faster while you build internal capability. We typically engage for 3-6 months to assess the current AI deployment landscape, build the security architecture (supply chain verification, trust boundaries, monitoring), red-team the critical systems, and document the program so your internal team can maintain it. The handoff is the goal. We build the program and the tooling; your team runs it. The cost of a 6-month engagement is a fraction of what a single AI-related breach costs or a biometric class action settlement (Texas extracted $2.8 billion from Google and Meta in 2025 alone).
How long does an AI security assessment take, and what does it cover?
A comprehensive AI security assessment typically runs 4-8 weeks depending on the number of AI systems in scope. Week one maps the AI inventory: every model in production, its provenance, deployment method, data flows, and access controls. Most organizations discover models they did not know were running. Weeks two through four cover adversarial testing against the MITRE ATLAS taxonomy and OWASP LLM Top 10 v2.0, including prompt injection (direct and indirect), supply chain integrity verification, data exfiltration testing, and privilege escalation through tool-calling interfaces. The final phase produces a prioritized remediation plan with architectural recommendations, not just a list of findings. We map every finding to applicable regulatory requirements (EU AI Act, NIST AI RMF, BIPA/CUBI if biometric systems are in scope) so the remediation simultaneously closes security gaps and compliance gaps.
What actually works against prompt injection in production?
No single defense reliably stops prompt injection. The space of possible injections is infinite while filters target finite patterns. Adaptive attacks against any single defense layer exceed 85% success rates in controlled testing. What works is layered architectural defense. Input validation catches the obvious attacks. Output validation with LLM-as-critic improves detection precision by 21% over input filtering alone (based on 600K+ adversarial prompts from the HackAPrompt dataset). But the structural controls matter most: separating untrusted content from privileged instructions at the architecture level, enforcing least-privilege permissions on tool-calling interfaces, requiring human approval for high-impact operations, and designing retrieval pipelines so that retrieved documents cannot override system-level instructions. For agentic systems specifically, trust boundaries between agents must be explicit and enforced, not assumed. We build these architectural controls into the system rather than bolting filtering onto the outside.
How do we secure our AI model supply chain when we use open-source models from Hugging Face?
Start by accepting that Hugging Face is a public registry, not a vetted supply chain. JFrog found roughly 100 malicious models with embedded code execution payloads. Palo Alto Unit 42 showed that deleted namespaces can be re-registered by attackers. Malicious LoRA adapters are indistinguishable from legitimate fine-tuning without integrity verification. The practical defense has four layers. First, never load pickle-serialized models in production; require safetensors format, which is not executable by design. Second, verify model provenance: check commit history, contributor reputation, and weight checksums against known-good baselines. Third, build an ML-BOM (machine learning bill of materials) using CycloneDX or SPDX 3.0.1 that tracks every model component's origin, version, and dependencies. Fourth, run automated scanning on every model update before it enters your CI/CD pipeline, and monitor upstream repositories for namespace changes or unexpected weight modifications. We build this verification pipeline as an integrated part of your MLOps workflow, not a separate manual process.
What are the EU AI Act security requirements for high-risk AI systems taking effect in August 2026?
The EU AI Act's high-risk requirements (effective August 2, 2026) mandate specific security controls including robustness against adversarial attacks, data governance for training datasets, technical documentation of the AI system's design and testing, human oversight mechanisms, and accuracy/reliability monitoring throughout the system lifecycle. Penalties reach EUR 35 million or 7% of global annual turnover for the most serious violations. The practical challenge is that the Act's requirements are principles-based, not prescriptive. 'Appropriate level of robustness' does not tell you which adversarial tests to run. We map the Act's requirements to specific technical controls: adversarial testing protocols aligned with MITRE ATLAS, supply chain integrity checks that satisfy the Act's transparency requirements, monitoring systems that generate the compliance evidence regulators expect, and documentation that traces from the regulatory requirement to the implemented control. Organizations that treat this as a compliance checkbox exercise will find the Act's enforcement mechanisms are designed to look through governance paperwork to the actual technical implementation.
How do we get visibility into shadow AI usage across our organization?
Shadow AI is the number-one operational AI risk right now. Research shows 69% of organizations suspect employees use unapproved GenAI tools, and the average company sees 223 incidents per month of sensitive data sent to AI applications. Shadow AI breaches cost $4.63 million on average, significantly more than standard breaches. Banning AI tools does not work; studies consistently show that employees bypass bans. The SANS Institute's 'Sunlight AI' approach is closer to the right answer: bring shadow usage into visibility rather than trying to prohibit it. Technically, this means deploying network-level detection for AI API traffic, building an approved-tools catalog with proper data classification controls, implementing DLP (data loss prevention) rules specific to AI service endpoints, and creating usage policies that give employees a sanctioned path for AI adoption. We build the technical monitoring layer and integrate it with your existing SIEM/SOAR stack so AI usage appears in the same dashboards your SOC already watches.
How do we secure agentic AI systems where agents call tools and make autonomous decisions?
Agentic AI introduces security problems that do not exist in single-model deployments. Controlled trials show 84% attack success rates against multi-agent systems versus roughly 50% for single-agent architectures. The core issue is trust propagation: when Agent A trusts Agent B's output and uses it to make tool calls, a compromise of Agent B's input (through indirect prompt injection in a retrieved document, for example) cascades through the entire agent network. MITRE ATLAS v5.4.0 now catalogs agent-specific techniques including poisoned tool publishing and host escape. The architectural defense requires explicit trust boundaries between agents, least-privilege permissions on every tool-calling interface (an agent that needs read access should never have write access), input sanitization at every agent-to-agent handoff, and human-in-the-loop gates for operations with real-world consequences. We design these trust architectures for specific agentic deployments, because the right boundary placement depends on what each agent does, what tools it can call, and what data it processes.
Should we use MITRE ATLAS or OWASP LLM Top 10 as our AI security framework?
Use both. They serve different purposes and are complementary. OWASP LLM Top 10 v2.0 (2025 edition) is a prioritized risk list for LLM applications: prompt injection, sensitive information disclosure, supply chain vulnerabilities, excessive agency, system prompt leakage, vector/embedding weaknesses. It tells you what to worry about first. MITRE ATLAS is an adversarial threat taxonomy with 16 tactics, 84 techniques, and 56 sub-techniques that tells you how attackers actually compromise ML systems. ATLAS maps attack chains; OWASP prioritizes risks. In practice, we use OWASP to scope what an assessment covers and MITRE ATLAS to structure how we test each risk area. For organizations building an AI security program, NIST AI 600-1 (the generative AI profile of the AI RMF) provides the governance wrapper that connects both frameworks to organizational risk management. The three together give you risk prioritization (OWASP), attack simulation methodology (ATLAS), and governance structure (NIST).
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Veriprajna Deep Tech Consultancy specializes in building safety-critical AI systems for healthcare, finance, and regulatory domains. Our architectures are validated against established protocols with comprehensive compliance documentation.