AI Security Services for Secure AI & GenAI Systems
Secure, govern and stress-test your AI from training data through to the model your customers talk to.
AI systems fail in ways traditional security controls were never built to catch. A model can be manipulated through a carefully worded prompt rather than an exploit. Training data can leak back out through inference.
A governance gap can surface as a discriminatory decision that a regulator later asks you to explain. LogiQuad secures AI end to end, from strategy and governance through architecture, regulatory readiness and adversarial testing across generative AI, machine learning and autonomous decision systems.
Our AI Security Services
AI Security Consulting
Advisory for organizations deploying AI faster than they can secure it. We build a security program that covers the full AI lifecycle: data sourcing, training, deployment, inference and retraining.
- AI security strategy, operating model and 12 to 36 month roadmap
- Secure AI development lifecycle (AI-SDLC) embedded in engineering workflows
- Threat modelling for adversarial ML, prompt injection and supply chain exposure
- Shadow AI discovery, unsanctioned models and tools already in use across the business
- AI security policies, standards and human-in-the-loop requirements
AI Governance, Risk & Compliance (AI GRC)
Accountability and oversight for systems making real decisions. We put structure around your models so leadership knows what is running, who owns it, and what it could cost if it goes wrong.
- AI governance charters, steering committees and model ownership
- Centralized model inventory with risk classification, high, medium, low
- AI risk register spanning security, privacy, bias, operational and legal exposure
- Responsible AI controls, fairness testing, explainability and transparency reporting
- Integration with existing ISO 27001 ISMS, enterprise risk and audit programs
AI Regulatory Readiness
Regulators and enterprise buyers ask for evidence, not intent. We build the documentation, classification and oversight mechanisms you will be asked to produce before you are asked.
- Regulatory applicability assessment across jurisdictions and industry guidance
- Risk classification and impact assessment for each AI use case
- Regulator-ready artifacts, model cards, data sheets, architecture and decision logic
- Human oversight design, override mechanisms, escalation and accountability tracking
- Alignment with GDPR, India's DPDP Act and emerging AI-specific regulation
Secure AI Architecture
Security designed into the platform rather than retrofitted after launch. We architect data pipelines, model lifecycle and inference layers so that the secure path is also the default one.
- Secure data pipeline design, ingestion, validation, lineage and encryption
- Model lifecycle controls, registries, versioning, integrity verification and approval gates
- Identity and access architecture across training, registry and inference environments
- API and prompt interface hardening, authentication, rate limiting, input validation, output filtering
- Secrets and key management, with MLOps and DevSecOps pipeline integration
AI Red Teaming
Adversarial testing built for AI, not borrowed from network penetration testing. We attack your models the way real users and attackers will, then show you exactly how they got through.
- Prompt injection, jailbreak and system-instruction extraction testing
- Sensitive data leakage and training-data reconstruction testing
- Model extraction and inversion testing to protect proprietary IP
- Abuse, misuse and unsafe-output scenario testing
- Proof-of-concept examples with root cause analysis and remediation guidance
How We Engage
- Assess (1 to 3 weeks). Discovery across your AI estate: models in production, data sources, interfaces and existing controls. You receive risk-ranked findings and a costed roadmap.
- Implement (4 to 12 weeks). Our specialists work alongside your data science and engineering teams designing controls and hardening systems, rather than filing tickets and walking away.
- Sustain (ongoing, optional). Red teaming ahead of major model releases, governance reviews as new use cases launch, and support when customers or regulators ask for evidence.
Who this is for
- SaaS and AI product companies facing enterprise security reviews and responsible-AI questionnaires
- Enterprises with AI deployed across multiple business units and board-level risk oversight
- Regulated businesses in banking, insurance, fintech, healthcare and telecom making high-impact automated decisions
- Startups building AI foundations early, ahead of investor due diligence and scale
What you get
- Risk-ranked findings report with proof-of-concept examples written for engineers, readable by your board
- AI model inventory with risk classification and named owners
- Governance framework, policies and human oversight procedures that operate, rather than sit in a folder
- Hardened data pipelines, model lifecycle and API controls implemented, not just recommended
- Regulator-ready documentation and a reusable answer set for AI security questionnaires
AI Systems We Secure
Generative AI and LLMs
Commercial model APIs, managed platforms such as Azure OpenAI, AWS Bedrock and Google Vertex AI, and self-hosted open-weight models.
Machine learning and predictive models
Fraud detection, credit and risk scoring, recommendation engines, computer vision and predictive analytics across training pipelines, registries.
AI agents and autonomous systems
Agent frameworks, tool-calling and orchestration layers, and autonomous decision engines where actions are taken without a human in the loop.
Third-party and embedded AI
Foundation model APIs, open-source frameworks and AI features embedded in SaaS products you already use, usually where visibility is weakest.
Deployment environments
Cloud-native, hybrid, on-premise and multi-tenant SaaS. Security principles hold consistently regardless of where the model runs.
Frequently Asked Questions
Do you work with generative AI and LLM platforms?
Yes. Generative AI and LLM-based applications make up the majority of our work, including retrieval-augmented systems and agent frameworks.
Is AI Red Teaming the same as penetration testing?
No. Penetration testing targets infrastructure and application flaws. AI Red Teaming targets model behavior (prompt injection, unsafe outputs, data leakage and model extraction), which standard testing does not assess.
We already have a cybersecurity GRC program. Why do we need AI GRC?
Traditional GRC assumes deterministic systems. It has no answer for model drift, probabilistic outputs, bias, or explaining an automated decision to a regulator. AI GRC extends what you already have rather than replacing it.
Do you work with models we did not build ourselves?
Yes. Foundation model APIs, open-source frameworks and AI features embedded in third-party SaaS are all in scope and often where organizations have the least visibility.
Find out how your AI behaves under pressure.
Book a free 30-minute AI security review with our team.
How can we help you scale?
We're excited to hear from you, and brew your digital success together!