Andersen applies offensive security, threat modeling, and vulnerability management to identify vulnerabilities faster and lower remediation costs.
AI Security Services
Andersen is a software and cybersecurity company delivering AI security services for assessments, testing, governance, and secure implementation. We reduce AI risk, support artificial intelligence security compliance, and help teams scale safe AI adoption.
Proven expertise in AI security
Our teams secure generative AI, embedded AI, and AI agents from design to controlled deployment, reducing risk across the AI lifecycle.
Andersen aligns security controls with ISO/IEC 27001, SOC 2, GDPR, and audit readiness needs for regulated sectors. Audits move faster with fewer evidence gaps.
AI security services we offer
Andersen secures AI systems through assessment, testing, and governance tied to measurable risk reduction.
Andersen assesses AI workflows, training data, inference infrastructure, and access controls for secure AI deployment. Teams receive ranked risks and fixes.
Deliverables:
- Inventory of models, owners, data flows, and third-party vendor risk;
- Scoring for compliance exposure, data leakage, and model behavior;
- Remediation roadmap mapped to business impact.
We build AI governance for organizations facing the EU AI Act, ISO 42001, GDPR, and sector rules. Leaders gain ownership, approvals, and control maturity.
Consulting output:
- Governance framework with owners, approval gates, and reporting;
- Acceptable use policy and security policies for AI initiatives;
- NIST AI Risk Management Framework mapping.
Andersen designs security-by-design architecture for LLM-powered tools, machine learning models, and AI supply chain integrations. Teams reduce attack surface before release.
Architecture work:
- Model access design, isolation boundaries, and least-privilege roles;
- Secure data pipelines, secrets handling, and monitoring points;
- Review of vendor APIs, plugins, and retrieval components.
Andersen tests large language models and generative AI features in chatbots, assistants, and internal tools. Teams reduce unsafe outputs and protect sensitive data.
Assessment areas:
- OWASP Top 10 for LLM Applications coverage;
- Guardrails, retrieval controls, hallucination risk, and unsafe tool use;
- Evidence for product, legal, and compliance review.
Andersen runs recurring attack-path checks for AI applications after releases, model updates, and vendor changes. Teams receive exploit proof, fix ownership, and lower regression risk.
Validation covers:
- Red team exercises for prompt injection, adversarial attacks, and model theft;
- Automated checks for APIs, authorization, secrets, and data encryption gaps;
- Recurring reports that keep vulnerability management current.
We discover shadow AI across SaaS, endpoints, repositories, and business tools. Leaders receive approved-use rules and blocking priorities.
Control plan:
- Discovery of unapproved AI tools, data sharing, and user groups;
- Risk tiers for sensitive data, departments, and processes;
- Policy, logging, and exception workflows.
Andersen turns AI risk signals into prioritized remediation and safer release decisions.
Our AI and security case studies
Andersen delivers AI products where security, access, privacy, and measurable business outcomes matter from the first architecture decision.
Austria
Securing data-driven forecasting for Quantics
For an Austrian AI company, Andersen built a forecasting MVP with roles, shared links, CSRF protection, and input sanitizing. The product helped the client win its first tender and acquire early users.
Who our AI security services are for
Andersen helps product, security, data, and compliance leaders secure AI use cases before defects become business issues.
Enterprises scaling AI
Enterprises deploying copilots across departments can create unmanaged data paths. Andersen maps usage, owners, and controls so expansion stays visible and risk does not grow with every rollout.
Organizations protecting sensitive data
Financial services, healthcare, and retail teams process personal, payment, and operational records. Andersen reviews data protection, masking, retention, and access so sensitive flows stay controlled.
Teams building AI features
Product teams adding assistants, RAG, or recommendations can miss abuse cases late in release. Andersen tests prompts, tools, and outputs so defects are fixed before launch.
Security teams managing AI risks
Security teams facing new cyber threats need telemetry, playbooks, and repeatable tests. Andersen defines signals and response paths so model misuse becomes visible and containable.
Regulated organizations
Healthcare, banking, and public sector teams must prove traceability across data, vendors, and decisions. Andersen prepares evidence and security workshops that reduce audit risk.
Companies preparing for the EU AI Act
Companies classifying high-risk use cases need policy, documentation, and ownership before review. Andersen builds routines that lower enforcement exposure and speed approval.
AI security risks we protect you from
Andersen maps artificial intelligence security risks to OWASP categories, business impact, and controls that lower exposure before attackers exploit AI systems.
OWASP LLM01 covers prompt injection and jailbreaking, where user or retrieved content overrides system instructions. Andersen tests prompt boundaries and guardrails so AI agents do not execute unsafe actions or expose data.
Business impact:
- Injected prompts can trigger false transactions or support actions;
- Validated prompt controls reduce legal and operational escalation.
OWASP LLM04 covers data poisoning and model poisoning through corrupted training data, feedback, or embeddings. Andersen validates source lineage so teams remove poisoned inputs before they distort decisions.
Business impact:
- Poisoned records can mislead advisors, agents, and staff;
- Validation and lineage controls lower correction costs.
OWASP LLM10 covers Unbounded Consumption, including extraction attempts that use repeated queries to infer model behavior, prompts, or proprietary outputs. Andersen reviews rate limits, access controls, and inference infrastructure so model theft risk drops.
Business impact:
- Unbounded calls can leak product logic or training patterns;
- Consumption controls protect intellectual property and service economics.
OWASP LLM02 covers Sensitive Information Disclosure through prompts, logs, retrieval, or responses. Andersen validates classification, redaction, data encryption, and retention so privacy exposure stays lower.
Business impact:
- Personal or payment data in prompts can create reportable data leakage;
- Masking and retention rules reduce breach scope.
OWASP LLM03 covers Supply Chain risks across models, datasets, libraries, plugins, and vendors. Andersen reviews AI supply chain dependencies and third-party vendor risk so unsafe components are blocked before release.
Business impact:
- Unverified models can introduce backdoors or unstable behavior;
- Controls reduce concentration risk and procurement surprises.
OWASP LLM06 covers Excessive Agency, where shadow AI or over-permissioned tools act outside approved rules. Andersen defines ownership, security policies, and approval paths so compliance exposure and unplanned spend drop.
Business impact:
- Unapproved tools can process regulated data without records;
- Governed access restores oversight and lowers audit findings.
Andersen tests AI risks against real abuse cases and turns findings into release-ready fixes.
Security frameworks, certifications, and AI expertise
Andersen aligns artificial intelligence security work with ISO/IEC 27001, ISO 42001, SOC 2, GDPR, NIST, and OWASP practices for audit readiness.
Business benefits of strong AI security
Strong AI controls protect revenue, compliance, and customer trust while keeping product teams able to ship.
Why choose Andersen for AI security services
Andersen combines cybersecurity delivery, AI product work, and compliance evidence tied to business outcomes.
Secure AI adoption with cybersecurity expertise
40+ security engineers and 120+ AI projects help teams reduce gaps without slowing delivery.
Certified specialists protecting AI and data
Andersen works with CISSP, CISM, CEH, OSCP, cloud, and data specialists across AI stacks.
Compliance-first delivery approach
ISO/IEC 27001, AICPA SOC 2, GDPR, EU AI Act, and ISO 42001 mappings support audits.
Full-cycle support from audit to remediation
Andersen handles assessment, architecture, testing, monitoring, and retesting in one delivery flow.
Industry-aware security for regulated sectors
Finance, healthcare, media, and enterprise AI cases cover payments, personal data, and cloud workloads.
Flexible engagement models
Teams can start with assessment, add red teaming, or keep Andersen for monitoring and response.
Our approach to securing AI systems
Andersen secures pre-launch and deployed AI through discovery, controls, hardening, monitoring, and retesting.
Andersen identifies AI systems, owners, data flows, vendors, and business goals. The client receives a scoped inventory that focuses testing budget on the highest-impact assets.
Testimonials
Andersen clients value transparent delivery, responsive engineering, and practical risk handling across AI, finance, and cloud products.
Use a practical EU AI Act guide to prepare AI compliance decisions
Insights and best practices in AI security
Andersen articles help security, data, and product teams turn AI risk and governance into daily engineering decisions.
Article
EU AI Act: Beyond the Compliance HurdleThe era of unregulated AI growth is over. Explore the mechanics of the EU AI Act and Andersen’s strategic framework. Use our roadmap to turn compliance into a high-integrity technical foundation for your enterprise.
Reading time: 5 mins
Article
Secure Federated Learning for Enterprise AIDiscover how federated learning helps enterprises train AI models without moving sensitive data and how distributed training, privacy-preserving techniques, and modern ML frameworks ensure intelligence across regions.
Reading time: 5 mins
Article
Securing Software-Defined VehiclesDiscover the key cybersecurity threats facing Software-Defined Vehicles (SDVs) and how developers can counter them. Learn what steps automakers take to secure modern vehicles and ensure safe, connected driving.
Reading time: 5 mins
Article
Why AI is Vital for Your Business StrategyThis article explores the role of AI in modern business settings. Discover how companies can develop AI implementation strategies and use smart digital solutions to enhance their operations.
Reading time: 8 mins
FAQ
AI application security protects models, prompts, data flows, APIs, agents, and user actions across an AI product. Unlike traditional application security, it also covers model-level attacks such as prompt injection and data poisoning, where behavior can be manipulated without changing source code.
Tell us what you need to secure
What happens next?
An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
Customers who trust us
