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People-First. AI-Amplified.

Is Your IT Ready for the AI Revolution?

Your AI Adoption & IT Readiness Blueprint for Life Sciences

As an IT Manager in life sciences, you're tasked with securely integrating AI to accelerate research and operations. This self-assessment helps you evaluate your organization's readiness for secure AI adoption, minimizing disruption and maximizing its transformative potential.

Assess Your AI Readiness & Security Posture

For each statement, select the option that best reflects your organization's current state. Your responses will help shape your personalized blueprint.

Scoring Guide:

  • 3 Points = Strong/Proactive (Fully implemented, optimized, strategic)
  • 2 Points = Moderate/Developing (Partially implemented, some gaps, evolving)
  • 1 Point = Weak/Reactive (Missing, ad-hoc, significant vulnerabilities)

SECTION 1: AI Infrastructure & Data Readiness

Evaluating your foundational IT for AI workloads and data management.

1.1 Our current IT infrastructure (compute, storage, network) is scalable and optimized to handle demanding AI/ML workloads.

1.2 We have high-quality, well-organized, and easily accessible data sets suitable for AI model training and deployment (e.g., clinical trial data, genomic data, research notes).

1.3 Our data governance framework includes specific policies for the collection, storage, use, and disposal of data used in AI systems, especially sensitive life science data.

1.4 We have a clear strategy for managing and optimizing cloud costs associated with AI development and deployment (e.g., GPU instances, data storage).

SECTION 2: AI Security & Compliance

Ensuring AI systems are secure, ethical, and compliant with life science regulations.

2.1 We have established security controls and processes for AI models themselves, including protection against adversarial attacks, model inversion, and data poisoning.

2.2 Our AI initiatives comply with relevant life science regulations (e.g., HIPAA for patient data, GxP for clinical data, FDA guidelines for AI/ML-based medical devices).

2.3 We have clear policies and procedures for ensuring data privacy, fairness, transparency, and accountability in our AI systems (ethical AI considerations).

2.4 Our Incident Response Plan includes specific protocols for AI-related security incidents, such as compromised models, data leakage from AI systems, or AI-driven attacks.

SECTION 3: AI Operational Integration & Talent

Integrating AI into daily operations and developing necessary IT skills.

3.1 We have a clear strategy for integrating new AI tools and systems into our existing IT operations, workflows, and monitoring tools.

3.2 Our IT staff receive ongoing training and development to acquire the necessary skills for supporting, securing, and managing AI systems.

3.3 We have robust monitoring and performance management systems in place for our deployed AI models to ensure their accuracy, reliability, and security over time.

3.4 Our IT budget includes dedicated allocation for AI-specific infrastructure, security tools, and talent development, with clear ROI tracking for AI initiatives.