AI Security Services

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 strategy and governance through architecture, regulatory readiness and adversarial testing across generative AI, machine learning and autonomous decision systems.

Our AI Security Services

Five service lines, delivered individually or as a complete programme. Most clients begin with a governance baseline or a red team exercise and expand from there.

AI Security Consulting

Advisory for organisations deploying AI faster than they can secure it. We build a security programme that covers the full AI lifecycle data sourcing, training, deployment, inference and retraining.

AI security strategy, operating model and 12–36 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

Centralised 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 programmes

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

Centralised 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 programmes

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 artefacts 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

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

Bias and fairness stress testing across edge cases and demographic groups

Proof-of-concept examples with root cause analysis and remediation guidance

How We Engage

Assess (1 - 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 (4–12 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 BFSI, 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

We are model-agnostic and vendor-neutral. Whether you built the model, fine-tuned someone else’s, or inherited it inside a SaaS product you bought, it is in scope.

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. Includes retrieval-augmented generation, chatbots and virtual assistants.

Machine learning and predictive models

Fraud detection, credit and risk scoring, recommendation engines, computer vision and predictive analytics across training pipelines, registries and inference endpoints.

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 behaviour prompt injection, unsafe outputs, data leakage and model extraction which standard testing does not assess.

We already have a cybersecurity GRC programme. 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 organisations have the least visibility.

Find out how your AI behaves under pressure.

Book a free 30-minute AI security review with our team.

AI Security Services

Secure, govern and stress-test your AI from training data through to the model your customers talk to

Our AI Security Services

AI Security Consulting

AI security strategy, operating model and 12–36 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)

AI governance charters, steering committees and model ownership

Centralised 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 programmes

AI Governance, Risk & Compliance (AI GRC)

AI governance charters, steering committees and model ownership

Centralised 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 programmes

AI Regulatory Readiness

Regulatory applicability assessment across jurisdictions and industry guidance

Risk classification and impact assessment for each AI use case

Regulator-ready artefacts 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

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

Secure AI Architecture

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

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

Bias and fairness stress testing across edge cases and demographic groups

Proof-of-concept examples with root cause analysis and remediation guidance

How We Engage

Who this is for