AI Risk Assessment
Assessing AI, Enabling Innovation
In the fast-changing tech environment of today, AI is an integral part of business operations in various industries. Although AI brings enormous advantages, it also presents novel security challenges that conventional risk models might not effectively address.
Why Do You Need AI Risk Assessment?
The integration of AI systems presents unique security concerns that go beyond traditional IT security paradigms. Organizations need to respond to regulatory compliance by fulfilling new AI-specific standards, while establishing stakeholder trust by showing responsible AI practices. In-depth checks reveal potential weaknesses in AI models that may result in impaired data or biased results, neutralizing legal risk through documented due diligence. As more sensitive tasks and data are undertaken by AI systems, the security failure stakes rise in proportion. A proactive risk assessment strategy enables you to reap the rewards of AI innovation while ensuring proper protection in place.
01
ISO 23894
It provides guidelines and systematic principles for the management of risks for AI systems throughout their life cycle. It emphasizes a systematic approach towards the identification and control of risks.
02
NIST AI Risk Management Framework (AI-RMF)
The AI-RMF offers a comprehensive methodology for addressing risks within AI systems through governance, mapping, measurement, and management processes.
03
HITRUST Risk Assessment Framework
Most beneficial in healthcare and sensitive data environments, this framework integrates AI risk factors with controls for general security.
Accorian’s Proven Approach

Scope Definition
The scope of an AI risk assessment ranges from the entire organization to specific divisions, systems, or individual AI components. It includes defining boundaries such as systems assessed, involved functions, applicable regulations, and business objectives. In tech areas, vulnerability assessments are also conducted to quantify AI risks. Regular, incremental evaluations ensure thorough coverage while adapting to evolving threats and major changes. This phase ensures alignment with organizational goals, asset protection, compliance, continuity, and cost-effective AI risk management.
Risk Identification
This process involves identifying, evaluating, and managing potential threats to AI systems and their data. Techniques such as architectural review, data flow analysis, and third-party assessments uncover issues like algorithmic bias, model poisoning, data breaches, or system failures. These threats are analyzed for likelihood and impact, and strategies are formed to reduce, transfer, accept, or avoid them.
Risk Treatment
Risk treatment addresses identified threats through acceptance, mitigation, or avoidance. Acceptance retains manageable AI risks; mitigation reduces threats with controls and training; avoidance removes activities causing unacceptable AI risks. Action plans are prioritized by severity and include technical, procedural, and AI governance enhancements. These practical, balanced strategies support secure, feasible implementation while strengthening overall AI risk posture.
Why Choose Accorian?
Accorian delivers specialized AI security expertise with practical business acumen. We provide accelerated timelines through proven methodologies, real-world implementation experience across industries, and purpose-built AI security tools. With Accorian, you avoid starting from zero in unfamiliar territory – instead gaining the roadmap and guidance needed for confident, effective AI risk management.