Data Management Glossary
AI and Corporate Data
Corporate data and AI are deeply intertwined, with businesses increasingly running artificial intelligence technologies to extract insights, automate processes, and enhance decision-making. As 90% of data that is being created everyday in the enterprise is unstructured data, it’s increasingly important to establish a comprehensive unstructured data management and data governance strategy.
Some areas where AI is transforming corporate data management include:
AI Data Management & Governance
- AI-powered Data Classification: Automatically categorizes structured and unstructured data.
- Data Quality & Cleansing: AI detects and corrects inconsistencies, missing values, and errors.
- Metadata Management: AI-driven tagging and indexing improve searchability and governance.
AI for Business Intelligence & Analytics
- Predictive Analytics: Forecasts trends, customer behavior, and operational needs.
- Natural Language Processing (NLP): Enables conversational AI-driven data querying.
- Automated Insights: AI finds patterns and correlations in vast datasets.
AI in Security & Compliance
- Anomaly Detection: Identifies fraud, security breaches, and operational risks.
- PII & Sensitive Data Protection: AI-driven redaction and masking.
- Regulatory Compliance: Automates policy adherence (e.g., GDPR, HIPAA).
AI-driven Automation & Optimization
- Intelligent Process Automation (IPA): Automates workflows, reducing manual tasks.
- AI-powered Data Pipelines: Automates the kind of workflows historically done by ETL (Extract, Transform, Load) tools, but now are being performed by a new set of modern technologies. Read interview with Komprise COO on AI data pipelines.
- AI for IT & Infrastructure Optimization: Enhances cloud cost management and resource allocation.
AI and Corporate Decision-making
- AI-assisted Decision Support Systems: Provides data-driven recommendations.
- AI-powered Financial Forecasting: Enhances investment and budgeting strategies.
- Customer & Market Intelligence: AI extracts insights from social media, surveys, and market data.
Risks of AI with Corporate Data
It is still early days for most companies when it comes to running AI on corporate data sets, but increasingly, data storage teams are responsible for data governance and compliance. Unfortunately most lack ways to do this systematically across their data estate.
Some of the risks associated with AI and corporate data include:
AI Data Privacy & Security Risks
- Unauthorized Access & Data Breaches: AI systems often process vast amounts of sensitive data, making them attractive targets for cyberattacks. The global average cost of a data breach reached $4.88 million in 2024, according to IBM.
- PII & Confidential Data Exposure: AI models may inadvertently expose personally identifiable information (PII) or proprietary business data. According to Menlo Security, attempts to input sensitive data such as PII into GenAI platforms represent over half of data loss prevention (DLP) events, followed by confidential documents (40%).
- Shadow AI Risks: Employees may use unauthorized AI tools, leading to compliance and security risks.
AI Bias & Ethical Concerns
- Algorithmic Bias: AI models trained on biased datasets can produce unfair or discriminatory outcomes.
- Ethical Decision-making Challenges: AI-driven decisions (e.g., hiring, lending, promotions) may unintentionally reinforce systemic biases.
- Lack of Transparency: Many AI models, especially deep learning, operate as “black boxes,” making it difficult to audit or explain decisions.
AI Compliance & Regulatory Risks
- Regulatory Violations: AI-driven data processing must comply with laws such as GDPR, CCPA, and HIPAA, and non-compliance can lead to heavy fines.
- AI-generated Content Risks: Incorrect or misleading AI-generated reports, summaries, or insights can result in legal liabilities.
- Data Retention & Ownership Confusion: AI models may retain or generate derivative data, complicating compliance with data deletion policies.
AI Data Integrity & Quality Risks
- Hallucination & Inaccuracies: AI models, particularly generative AI, may produce incorrect, outdated, or misleading results.
- Data Poisoning: Attackers can manipulate training data to skew AI outcomes, leading to faulty insights or decisions.
- Over-reliance on AI: Automating decision-making without human oversight can lead to critical errors if the AI model is flawed.
AI Operational & Financial Risks
- Model Drift & Performance Degradation: AI models degrade over time as real-world data changes, requiring ongoing monitoring and retraining.
- High Implementation & Maintenance Costs: AI adoption requires significant investment in infrastructure, talent, and governance frameworks.
- Job Displacement & Workforce Challenges: Automating tasks with AI can create resistance from employees and require reskilling initiatives.
AI Intellectual Property & Competitive Risks
- Data Dependency on Third-Party AI Providers: Businesses using third-party AI solutions risk vendor lock-in and loss of control over their data.
- IP Ownership Issues: AI-generated content and insights may create legal disputes over intellectual property rights.
- Competitor AI Spying & Model Inference Attacks: AI models can be reverse-engineered, exposing proprietary data patterns or trade secrets.
Mitigating AI Risks in Corporate Data
To minimize these AI risks, organizations should implement robust AI governance frameworks, including:
- Data encryption & access controls to protect sensitive information.
- Bias audits & fairness checks for AI models.
- Human oversight & explainability in AI decision-making.
- Regulatory compliance tracking to adapt to evolving laws.
- Cybersecurity measures to prevent AI data leakage and cyber attacks.
