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AI Component Security

Ensures every layer of your AI infrastructure is resilient against manipulation, leakage, and unauthorized use.

AI Component Security

Uncovered Security Gaps

64%

Missed Vulnerabilities

41%

Incomplete Risk Visibility

58%

Common Security Risks

Attackers reconstruct training data or steal proprietary models using black-box API calls.

Subtle manipulations to input data fool the model into incorrect outputs—critical in AI used for vision, security, or healthcare.

Third-party libraries used for training or inference may contain exploitable code.

Model-serving APIs without proper auth or throttling can be abused, overloaded, or accessed for malicious use.

Data pipelines compromised to introduce bias, backdoors, or security flaws into model behaviour.

Our AI Security Testing Methodology

Static & Dynamic Security Analysis
Code reviews for pipelines, model logic, and framework configs, endpoint scanning for unauthorized access or data leaks.
Attack Simulation
Adversarial testing (FGSM, PGD, DeepFool), model extraction and fingerprinting tests, inference manipulation and API fuzzing, data poisoning simulations.
Pipeline Security
CI/CD & MLflow review, storage access validation (S3, GCS, etc.), access control, and logging audits.

What You’ll Receive

AI threat landscape mapping (specific to your stack).
Model & data-specific vulnerability report.
Secure model deployment checklist.
Recommendations for access control, audit logging, encryption, and hardening.
Compliance alignment (ISO 42001, NIST AI RMF, GDPR).

Why Cyber Shakthi?

Deep expertise in both AI architecture and cybersecurity.

Tailored testing for production, R&D, or cloud-deployed AI systems.

Leveraging OpenAI, Vertex AI, SageMaker, and more.

Focus on confidentiality, integrity, and ethical AI deployment.

Cyber threats bankrupt businesses every day. Be wise. Defend yours now.

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