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Enterprise Service

AI Security & Cybersecurity

Secure AI systems across models, data and integrations.

Strategic Overview

Assess and harden AI applications against prompt injection, data leakage, insecure integrations, and related LLM risks.

The Core Challenge

Problem Solved

LLMs introduce new attack paths that traditional application security reviews often miss.

Key Deliverables

  • AI security assessment report
  • Threat model for LLM applications
  • Prompt-injection and abuse testing findings
  • Remediation recommendations
  • Security control backlog

Execution Methodology

1. Attack surface mapping
2. Testing and review
3. Findings and remediation plan
4. Control implementation support
5. Recheck and playbook handover

Service Capabilities

Core skillsets and operational domains covered in this practice area.

  • AI security assessments
  • LLM threat modelling
  • Prompt injection testing
  • Data protection review
  • Identity and access review
  • AI red teaming
  • Cloud and API security for AI systems

Flexible Engagement Models

Available as a targeted advisory sprint, a fixed-scope implementation project, an embedded engineering pod, or fractional CAIO support.

Schedule Service Scoping Session

Frequently Asked Questions

How is AI security different from traditional cybersecurity?

AI systems blend instructions and data in natural language, so reviews must cover prompts, retrieval context, model behavior, plugins and supply-chain risk, not only static code.

Initiate Your AI Security & Cybersecurity Program

Connect directly with an AkonnAI practice leader to discuss scope, timelines, and business case justification.

Book an AI Strategy Session