AI Security Services

TechMagic delivers AI security services through a dedicated practice, with CREST-accredited penetration testing and engineers who run ISO 27001 and SOC 2 programs. We secure AI adoption across the software development lifecycle for engineering teams embedding AI features or scaling AI coding assistants. Reviews run inside your sprints, so delivery doesn't slow down.

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Who We Help Adopt AI Securely

TechMagic works with software companies that integrate AI into live products but lack dedicated in-house AI security expertise. That gap turns urgent when an AI-powered feature touches regulated data, or when an enterprise buyer asks for evidence nobody has collected.

FinTech companies

FinTech products handle card and account data under PCI DSS, SOC 2, and GDPR. Fraud detection, underwriting models, and chat assistants read sensitive data at inference time, where it resurfaces in prompts, logs, and outputs. We test those models for data exposure and control what the coding assistants can reach, so customer financial records stay inside your environment.

HealthTech companies

HealthTech products handle protected health information (PHI) under HIPAA, usually with a business associate agreement in force. Patient-facing and diagnostic features expose PHI through model inputs, outputs, logs, and retention settings. Coding assistants expose it through repositories and test fixtures holding real records. We trace how PHI moves through the model and the toolchain, then close the paths that expose it.

SaaS companies

SaaS teams move fastest on AI. Features ship on short release cycles, and most engineers get coding assistants from day one. A security process that adds days to a release gets worked around. We put controls inside your CI/CD pipeline, where they automate routine tasks such as dependency checks and secrets scanning, keeping the security posture current without slowing releases.

Enterprises

Enterprises rarely have one AI project. They run AI product features, coding tools, and internal agents at once, usually adopted independently by each team. These systems span hybrid cloud environments and connect to billing, customer records, and other critical systems. We assess each AI system in use and set governance controls that hold across teams, with ISO 42001 as the framework.

Other industries

Whatever the sector, the questions stay the same: what data reaches the model, what it can act on, and who signed off. HR-Tech products push candidate data through screening models. MarTech tools generate content at scale, where output handling and prompt injection are the main concerns. We run the same assessment and testing work in any sector, adjusted to the applicable regulations.

Certified by Industry-Leading Standards

Certified AI and ML Pentester by The Secops Group

CREST Security Testing - Penetration Testing

ISO 27001 Certified Implementer certification badge

Web Application Penetration Tester eXtreme Certificate

ISO 27001 Lead Auditor certification badge

Certified Cloud Pentesting Expert – AWS certification badge by The SecOps Group

CCSK v4 Certificate of Cloud Security Knowledge certification badge by Cloud Security Alliance

CompTIA CySA+ Cybersecurity Analyst certification badge

eMAPT Mobile Application Penetration Tester certification badge

Challenges We Solve as an AI Security Services Company

TechMagic solves AI-specific risks: shadow AI in the dev workflow, vulnerable AI-generated code, prompt injection, missing AI governance, supply chain and third-party model risk, and regulatory exposure. Generic application security assumes a person wrote the code, and AI consulting stops at model quality, so neither covers these.

Shadow AI in the dev workflow

Shadow AI is AI tooling used inside a company without approval, review, or visibility. In engineering, it usually means coding assistants installed personally, pointed at production repositories, with training and retention settings nobody checked. Every unreviewed tool adds to the organization's attack surface. We inventory what's actually in use, then set permissions and data rules engineers will actually follow.

Vulnerable AI-generated code

Large Language Models write code that looks right and compiles cleanly. Review catches less than teams expect, because code volume climbs while reviewer attention stays flat. The same defects recur: missing authentication checks, unsafe data handling, hardcoded secrets, and vulnerable dependencies, which evade detection under default scanner settings. We harden the coding pipeline itself, because that is where the risk starts.

Prompt injection and unsecured LLM and agent integrations

Prompt injection is an attack where hostile instructions hidden in content reach the model and change what it does. The damage depends on the model's reach. AI agents with code execution, database queries, or API access act on that instruction, turning a nuisance into a breach. LLM security testing here is manual work scanners can't do, so we test those integrations by hand.

Missing AI governance and accountability

Most teams can't say which AI systems run in production, who approved them, or what data they touch. The gap is structural, because AI arrives through product teams, platform teams, and individual engineers. AI governance closes it with an inventory, named owners, and a review path for new use cases. Without that, enterprise buyers stall in procurement and auditors have nothing to test.

AI supply chain and third-party model risk

Every model, framework, and plugin in an AI stack is someone else's code. Language models also invent package names that don't exist, and attackers register those names and publish packages under them. Training data and model artifacts add supply chain surface that ordinary vulnerability management never looked at. We scan for it and set policy on which providers and models are allowed.

Regulatory exposure across SOC 2, HIPAA, GDPR, and ISO 42001

AI questions now sit inside security questionnaires and audits. Buyers ask whether customer data trains your models, how agent identities are governed, and what evidence backs either answer. Regulatory compliance work on AI is mostly evidence work, and it has to exist before an auditor asks. We map AI controls to SOC 2, HIPAA, GDPR, and ISO 42001, then produce the evidence auditors accept.

Our AI Security Services

Our AI security services cover the full AI lifecycle inside a working product: the models and agents you ship, the pipeline that builds them, and the evidence an auditor will ask for. Each engagement below runs on its own or as part of an ongoing program. We agree on scope, deliverables, and exclusions before work starts.

AI security assessment

An AI security assessment maps every AI system you run and rates the AI risk each carries. We inventory models, agents, integrations, and data flows, then apply STRIDE and MITRE ATT&CK Enterprise and MITRE ATLAS in live sessions with your team.

The review covers how Machine Learning algorithms handle untrusted input, how Machine Learning models are trained, and where inference-time data leaks occur. You get a ranked list of findings and a threat model that drives later test cases and security measures.

AI security assessment

AI security testing

AI security testing validates an AI feature against the attack scenarios in its threat model. Dynamic application security testing (DAST) runs with Burp Suite, OWASP ZAP, or Caido, and AI-assisted triage sorts the output.

Our engineers then test the highest-risk paths by hand, and teams already running a security program can add AI red teaming and AI penetration testing. Prompt injection, jailbreak, and model extraction get chained through an agent's tools, and findings record how far an attacker gets.

Secure AI implementation and code review

We build the pipeline that makes secure AI development automatic. Static application security testing (SAST) with Semgrep and SonarQube runs in every pull request, Snyk covers dependency and supply chain scanning, and TruffleHog catches secrets before commit.

AI-generated code at volume needs more, because one long review session loses context. Our four-stage pipeline maps the source code, threat models against that map, scans priority attack surfaces, then validates in a fresh session, so each finding arrives with evidence and a fix.

Secure AI implementation and code review

AI coding assistant governance

AI tools in the IDE run on shared skills and command configurations that carry your security standards, regardless of which assistant the team uses. On top of that, we configure AI assistant workspaces loaded with your policies, architecture diagrams, and past findings.

Those workspaces review each feature requirement against SOC 2, OWASP, and CIS, then produce a matching security requirement for every functional one. We scope agent permissions so a tool can't read, execute, or change anything outside its boundary.

Pipeline and deployment hardening

One misconfigured deployment undoes months of secure development work. We scan Infrastructure-as-Code across Terraform, CloudFormation, and CDK, catching open security groups, permissive IAM roles, and unencrypted storage.

CI/CD variables, parameter stores, and secret managers then get scoped and rotated, and hardened base images such as Docker Hardened Images stay patched for critical and high-severity CVEs. We review service accounts, execution roles, and inter-service permissions against least privilege, so nothing in the deployment has more access than it needs.

AI governance and compliance

AI governance settles the security strategy before engineering starts and produces the audit evidence afterward. AI adoption advisory comes first, defining which AI technologies and use cases are approved before launch.

That written policy then anchors the AI management system (AIMS) an ISO 42001 audit assesses: system inventory, named ownership, acceptable-use policy, model approval paths, and control mapping. The same groundwork answers AI questions inside SOC 2 and HIPAA audits, so one body of evidence serves several frameworks.

ISO 42001 preparation services

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Our AI Security Process

Security starts at the requirements stage and continues through release. The steps below run in order, each feeding the next. Securing AI works best when the controls arrive with the feature.

Requirements and architecture review

We start with your feature requirements, architecture diagrams, and existing policies. Our engineers and your team work through them together, so we write security requirements before any code exists. That makes the requirements phase a joint security and engineering activity, and the output feeds the threat model.

AI threat modeling

Then, we build the threat model on top of that output, using STRIDE and MITRE ATT&CK. AI produces the first pass, and our engineers validate it, which turns days of workshops into hours. We map every AI-specific attack path to the component it targets.

Risk prioritization

Next, we rank what the model produced. We calibrate severity to your real environment, because a vulnerable package inside a hardened, network-isolated container carries different risk than the same package on an exposed service. You end up with a short, ordered list of what to fix.

Security by design in development

After that, the guardrails go into the coding workflow, so the requirements from steps one and two reach the assistant that writes the code. Rule-based checks run alongside, because deterministic tooling doesn't depend on model confidence. Developers see findings in the pull request while the change is still open.

Testing and controlled rollout

Before release, we test the feature against the attack scenarios in the threat model, combining automated scanning with manual work on the highest-risk paths. We review deployment configuration, secrets hygiene, and service account permissions at the same time, so the infrastructure is as governed as the code.

Retesting and continuous improvement

After remediation, we retest and confirm each fix holds. Threat models get updated as features change, and pipeline gates get tuned as new patterns appear. Hands-on sessions with your QA and development teams keep the method inside your team, so the next cycle needs less of us.

Certifications and Compliance Frameworks We Align With

TechMagic aligns AI security work with OWASP standards, STRIDE, MITRE ATT&CK, ISO/IEC 27001, ISO 42001, and the evidence behind SOC 2, HIPAA, and PCI DSS. Compliance is usually what starts the conversation, so each framework below is paired with what it delivers.

Security Experts Behind Your AI Security

Ihor Sasovets

Lead Security Engineer

Ihor is a certified security specialist with experience in penetration testing, security testing automation, cloud and mobile security. OWASP API Security Top 10 (2019) contributor. OWASP member since 2018.

Certified AppSec Practitioner certification badge by The SecOps Group

AWS Certified Security – Specialty certification badge

AWS Certified Cloud Practitioner certification badge

eWPT v1 eLearnSecurity Web Application Penetration Tester certification badge

Certified Cloud Security Practitioner – AWS certification badge by The SecOps Group

Blue Team Level 1 Tester certification badge

eJPT Junior Penetration Tester certification badge

Certified Mobile Pentester – Android certification badge by The SecOps Group

EC-Council Certified Ethical Hacker (CEH) certification badge

Certified AI/ML Pentester certification badge by The SecOps Group

Victoria Shutenko

Security Engineer

Victoria is a certified security specialist with a background in penetration testing, security testing automation, AWS cloud. Eager for enhancing software security posture and AWS solutions

eWPTX eLearnSecurity Web Application Penetration Tester eXtreme certification badge

Certified Network Pentester certification badge by The SecOps Group

Certified Network Security Practitioner certification badge by The SecOps Group

Denys Spys

Security Engineer

Denys is a certified security specialist with web and network penetration testing expertise. He demonstrates adeptness in Open Source Intelligence (OSINT) and executing social engineering campaigns. His wide-ranging skills position him as a well-rounded expert in the cybersecurity industry.

TCM Security Practical Junior Penetration Tester (PJPT) certification badge

TryHackMe Certified PT1 certification badge

arcX CTI Practitioner certification badge

eCPPT eLearnSecurity Certified Professional Penetration Tester certification badge

Certified AppSec Pentester certification badge by The SecOps Group

Roman Kolodiy

Director of Cloud & Cybersecurity

Roman is an AWS Expert at TechMagic. Helps teams to improve system reliability, optimise testing efforts, speed up release cycles & build confidence in product quality.

Project Management Professional (PMP) certification badge

AWS Certified DevOps Engineer – Professional certification badge

Can You Answer Your Buyer's AI Security Questionnaire?

Start with an AI security assessment. Talk to our security engineers and you'll have scope and timeline before anything is signed.

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Case Studies: Fewer False Positives, Faster Compliance

From 300,000 scanner findings to a backlog the team could explain line by line

Challenge.

A client preparing for its SOC 2 audit turned on AWS Inspector across AWS accounts and got 300,000+ findings within hours. The inspector reports every vulnerable package it finds, but can't say which ones an attacker can reach, so the team had no way to distinguish real exposure from noise.

Solution.

We traced most findings to a small set of vulnerable base images shared by every account, then fixed the pattern first in the two highest-priority accounts. Priority workloads moved to hardened base images, and Lambda dependencies were patched directly. Findings the environment made hard to exploit were risk-rated, not ignored: each suppression was recorded with a rationale and a named owner.

Impact.

Open critical findings fell from 300,000 to 100 in 7 days, and every remaining item had a documented decision behind it. The client went into its audit with a vulnerability backlog it could explain line by line.

A four-stage pipeline for reviewing AI-generated codez

Challenge.

A product team on Claude Code had built its own skills and commands library. Delivery outpaced security review, and long single-session reviews lost context and gave inconsistent results.

Solution.

We split the review into four bounded stages: source code mapping, threat modeling, a vulnerability scan across priority attack surfaces, and validation in a fresh session. Validation alone removed nine false positives.

Impact.

Roughly 80% fewer false positives than the single-session approach, on about 20% of the standard token budget. We fixed every confirmed vulnerability, all moderate or low severity, including broken access control and hardcoded secrets.

Why Choose TechMagic as Your AI Security Services Company

Engineers who help write the AI security standards

Our engineers are credited contributors to the OWASP API Security Top 10 and the OWASP Autonomous Penetration Testing Standard for agentic and AI-driven security testing. They serve on EC-Council item-writing committees that define AI security certification curricula and present at the OWASP GenAI & Agentic Security Summit, ContinuumCon, BSides, and AWS community events. Our Lead Security Engineer tested Kiro, Amazon's AI IDE, in its first cohort and fed findings to the product team.

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An in-house team certified across AI, cloud, and application security

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A framework you can adopt one stage at a time

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