AI introduces attack surfaces that traditional security tools weren't designed to handle. We architect security into every layer of your AI stack from day one — covering prompt injection, data leakage, model theft, and regulatory compliance for the most demanding regulated industries.
Traditional WAFs, firewalls, and DLP tools don't understand AI-specific attack vectors. These are the threats we defend against — and they require purpose-built AI security controls.
Malicious inputs override AI system instructions, causing models to reveal confidential information, ignore safety controls, execute unintended actions, or expose hidden system prompts. These attacks can be delivered directly through user interactions or indirectly through external documents and retrieved content in RAG systems.
Differential privacy techniques during fine-tuning, training data audits, memorisation testing before deployment, and output monitoring for training data reconstruction patterns.
AI systems can inadvertently expose confidential data in their responses — including PII from other users, proprietary business information, training data, or sensitive content from retrieved documents.
Output scanning with PII detection, response content policies, user-scoped retrieval in RAG systems, and data classification controls that prevent cross-user data exposure.
Adversarial queries can extract memorised sensitive data from fine-tuned models — including PII, trade secrets, or confidential information present in training datasets that was never intended to be accessible.
Differential privacy techniques during fine-tuning, training data audits, memorisation testing before deployment, and output monitoring for training data reconstruction patterns.
Without granular RBAC, users can query AI systems for data or capabilities beyond their authorisation level — particularly dangerous in multi-tenant enterprise deployments with varied user permissions.
Role-based access control at query, retrieval, and action levels. User-scoped vector DB partitions. Session-level permission enforcement. Privileged action approval workflows.
Systematic querying can reconstruct model weights, replicate fine-tuned capabilities, or extract proprietary prompt architectures — stealing the AI investment you've made without accessing your infrastructure.
Query rate limiting, output watermarking, API authentication and rotation, adversarial query detection, and obfuscation layers that prevent systematic model extraction attempts.
AI systems depend on third-party LLM APIs, vector databases, and orchestration libraries — each representing a potential security or compliance risk if not properly vetted and contractually secured.
Vendor security assessments, zero-retention API configurations, sub-processor DPA chain, dependency vulnerability scanning, and private deployment options for critical workloads.
We don't apply a checklist at the end — we architect security into every layer of the AI stack from the first line of code.
Every query inspected before reaching the AI model — detecting injection patterns, classifying intent, and blocking malicious inputs in under 5ms.
Multi-factor authentication, API key management, and granular role-based access control at query, retrieval, and action levels.
TLS 1.3 in transit, AES-256 at rest, zero-retention API configurations, and data residency enforcement for regulated data.
Automatic detection and anonymisation of PII, PHI, and sensitive data before it enters any AI model — with reversible pseudonymisation where needed.
Need additional senior engineers for a critical sprint? Need to reduce team size after a major release? Team capacity is reviewed quarterly and adjusted based on your business needs — without penalties, renegotiations, or unnecessary complexity.
Every AI interaction, access event, and administrative action logged with full context — tamper-evident, time-stamped, and queryable for compliance review.
24/7 anomaly detection, automated threat response, and a documented incident response plan with 15-minute P1 response SLA.
Every user input passes through a multi-stage validation pipeline before reaching your AI model. We detect and neutralise both direct prompt injection (user input attacks) and indirect injection (malicious content in retrieved documents for RAG systems).
Every compliance framework we support is fully implemented — not just "aligned." We produce the documentation and technical controls that pass actual audits.
Service Organisation Control 2
The gold standard for SaaS and cloud service security. Our AI systems are architected to satisfy all five Trust Service Criteria — Security, Availability, Confidentiality, Processing Integrity, and Privacy.
General Data Protection Regulation
EU data protection law applies to any organisation processing EU residents' personal data. AI systems that process user inputs carry significant GDPR exposure if not properly configured.
Health Insurance Portability & Accountability Act
US federal law governing protected health information (PHI). AI systems processing clinical notes, patient records, or any health data must implement specific technical safeguards.
Information Security Management
The international standard for information security management systems (ISMS). Increasingly required by enterprise procurement teams as a baseline vendor security requirement.
Digital Personal Data Protection Act, India
India's new data protection law (2023) introduces GDPR-like obligations for organisations processing Indian residents' personal data — including AI systems used by Indian enterprises.
Indian Financial Sector Regulations
RBI and SEBI have issued guidance on AI use in financial services — covering model risk management, explainability requirements, data governance, and operational risk controls for AI-driven decisions.
Every data movement is controlled, encrypted, logged, and governed. Nothing reaches an AI model that hasn't passed through multiple security checkpoints.
Authenticated session · TLS 1.3 · API key validated
Injection check · PII detection · Intent classification
User-scoped vector search · No cross-user data access
Anonymised data · Zero-retention config · Encrypted transit
Sensitive data scan · Policy check · Content validation
Clean, validated response · Full interaction logged
These aren't aspirational — they are technical controls enforced in the architecture of every AI system we build.
Only the minimum data required for the AI task is processed. PII is anonymised before reaching any model or external API.
Data collected for one purpose cannot be used by the AI for another — enforced at the system architecture level, not just policy.
Every request is authenticated and authorised at every layer — no implicit trust based on network location or prior sessions.
Every data access, AI interaction, and security event is logged with full context — supporting any compliance audit request within hours, not weeks.
Data never leaves your permitted jurisdictions. For highest-sensitivity use cases, fully air-gapped on-premise deployment with no external API calls.
Every AI interaction, access event, model change, and security incident is logged in a tamper-evident, queryable audit trail — in the format your auditors actually need.
Every query and AI response logged with full context — user identity, timestamp, permissions, and the complete interaction in a tamper-evident audit trail.
Every authentication event, RBAC check, permission grant, and access denial recorded with complete context.
Every model update, prompt modification, configuration change, and deployment event recorded with rollback history.
Each regulated industry has unique compliance obligations, risk tolerances, and audit requirements. We build AI security architectures that satisfy them all — not just generic enterprise security.
Clinical AI must handle PHI with zero tolerance for exposure while maintaining healthcare-grade security controls.
AI in financial services must satisfy model risk management requirements, produce explainable outputs for regulated decisions, and maintain audit trails that withstand regulatory examination.
Industrial AI systems touch operational technology (OT) networks where security failures can have physical consequences. We implement strict network segregation between AI systems and OT infrastructure.
Legal AI handles attorney-client privileged information and highly sensitive transaction data. We implement matter-level access controls and confidentiality boundaries that respect professional privilege obligations.
Zero data breaches. Zero compliance failures. Zero unresolved security incidents across 150+ enterprise AI deployments.
A top-10 law firm's contract review automation handles 300+ contracts per week — reducing per-contract review from 4 hours to 18 minutes with full clause extraction and risk flagging.
RBI's AI guidance is detailed and our initial vendor couldn't meet it. Aeologic understood exactly what model risk management documentation we needed, implemented explainable AI outputs, and built the audit trail our examiners required. Passed examination with no observations.
We process EU customer data through our AI system. Our DPO was concerned about GDPR exposure from LLM API calls. Aeologic configured zero-retention settings, implemented the full DPA chain with all sub-processors, and documented our DPIA. Our DPO signed off within a week.
Questions from CISOs, CTO, DPOs, and compliance teams evaluating AI security and compliance capabilities.
Enterprise AI systems introduce attack surfaces that don't exist in traditional software: prompt injection attacks (malicious inputs that hijack AI behaviour), model inversion attacks (extracting training data from model outputs), data leakage through AI responses, indirect prompt injection via retrieved documents in RAG systems, and unintended data memorisation in fine-tuned models. These require AI-specific defences layered on top of standard application security practices — traditional WAFs, firewalls, and DLP tools are insufficient on their own.
Prompt injection is an attack where malicious content in user inputs or retrieved documents attempts to override an AI system's instructions — causing it to reveal confidential data, bypass safety controls, or perform unauthorised actions. Defence requires multiple layers: ML-based injection pattern detection at the input layer, system prompt hardening that separates instructions from user-controlled content, privilege separation so the AI can't access resources beyond what each request requires, output filtering that catches anomalous responses, and sandboxed tool execution for AI agents so they can't take dangerous actions even if successfully manipulated.
GDPR compliance for AI systems requires: a signed DPA with Aeologic as your data processor, DPAs with all AI API providers (OpenAI, Anthropic, etc.) as sub-processors, zero-retention API configurations ensuring LLM providers don't store your data beyond processing, PII detection and anonymisation before data reaches any LLM, data residency enforcement ensuring EU personal data stays in the EU, data subject rights implementation (right to erasure works even for data used in AI interactions), and a DPIA for high-risk AI processing activities. We implement and document all of these as standard practice.
Yes — signing a comprehensive DPA is a non-negotiable prerequisite before any engagement begins, including discovery conversations where you might share business context. Our standard DPA covers: scope and purpose of data processing, types of personal data processed, security measures implemented, sub-processor relationships (including all AI API providers), data breach notification obligations (72-hour SLA), data deletion obligations on contract termination, and international transfer safeguards. Our DPA is reviewed annually by our legal team and is updated to reflect regulatory developments including the DPDP Act and any amendments to GDPR guidance on AI.
Yes. For organisations requiring full data sovereignty, we deploy AI systems using open-source models (LLaMA 3, Mistral, Falcon, Gemma) on your own infrastructure — on-premise or private cloud. All AI inference happens within your network perimeter. No data is sent to external APIs under any circumstance. The monitoring and observability stack is also self-hosted (Grafana, Prometheus) ensuring zero data egress. We achieve 80–95% of the capability of cloud API models for most enterprise use cases, while fully eliminating third-party data processor relationships.
Every production deployment includes a written Incident Response Plan covering AI-specific scenarios (prompt injection attacks, data leakage attempts, model drift exploits) alongside standard infrastructure incidents. P1 incidents (active data breach or critical system compromise) receive a 15-minute response with immediate escalation to your security team. Known attack signatures trigger automatic responses (blocking, alerting, rate-limiting) without waiting for human review. All incidents are documented in a post-incident report within 48 hours, covering root cause, timeline, impact assessment, and remediation measures. For regulated industries, we support your breach notification obligations to supervisory authorities.
Every AI system we deploy undergoes security testing before go-live covering: automated vulnerability scanning of all infrastructure components, manual prompt injection testing using current attack taxonomies (OWASP LLM Top 10), RBAC boundary testing (verifying users cannot access data beyond their permissions), PII detection bypass testing, output filtering evasion testing, and load-based security testing. For SOC 2 and ISO 27001 compliant deployments, we provide test reports in formats accepted by external auditors. We also offer post-deployment penetration testing on a quarterly basis for clients requiring continuous assurance.
AI security is evolving rapidly — new attack techniques emerge regularly. We maintain current threat intelligence through active participation in AI security communities, monitoring of OWASP LLM Top 10 updates, tracking published AI security research, and direct threat intelligence from our monitoring across 150+ deployed systems. When new attack patterns are identified, we push security control updates to all client systems within 48 hours for critical threats. We also conduct quarterly security reviews for all clients to assess whether their current controls remain adequate against the evolving threat landscape, and recommend updates proactively.
Tell us about your AI systems and compliance requirements. We'll identify your specific security gaps, map the applicable regulatory obligations, and recommend a remediation plan — all in writing, within 48 hours.
We review your current or planned AI architecture against the OWASP LLM Top 10 and applicable compliance frameworks — identifying every gap.
A written report mapping your AI systems to every applicable regulation — GDPR, HIPAA, SOC 2, RBI, SEBI, ISO 27001, DPDP — with specific control recommendations.
If you proceed, a fully secured and compliance-aligned AI architecture is implemented within 4 weeks of engagement start — built-in, not bolted on.
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