Artificial Intelligence (AI)

Artificial intelligence (AI) is a multidisciplinary field of computer science devoted to creating systems that can perform tasks typically requiring human intelligence. These tasks include reasoning, learning, problem-solving, perception, natural language understanding, and decision-making. Early efforts in AI focused on symbolic logic and rule-based systems, but the field has advanced to encompass machine learning, neural networks, and deep learning, wherein systems improve their performance by learning from vast amounts of data rather than explicit programming (OWASP LLM/AI Security Glossary, Brookings Institution).

Essential characteristics of AI include:

• Reasoning: Ability to draw inferences or conclusions from available information.
• Learning: Utilization of data (via machine learning) to update models and improve over time.
• Problem-solving: Identifying solutions to complex or ambiguous situations.
• Perception: Processing sensory data—such as images or sounds—as humans might.
• Language understanding: Comprehending and generating human language, as in natural language processing.

AI technologies are integrated into applications like virtual assistants, autonomous vehicles, medical diagnostics, robotics, fraud detection, and recommendation engines. A recent development is the rise of large language models (LLMs), which power advanced chatbots and content generators.

Types of AI can be broadly categorized as:

• Narrow (or Weak) AI: Systems highly specialized for a specific task (e.g., facial recognition, spam filters).
• General (or Strong) AI: Still theoretical, these systems would possess generalized intelligence matching human cognitive capabilities.
• Applied AI: AI focused on practical, real-world tasks, usually within a narrow context.

AI’s proliferation raises important challenges regarding security, privacy, reliability, and ethics. AI systems are subject to various risks, including adversarial attacks, model theft, data poisoning, and unauthorized data leakage. Maintaining the integrity, confidentiality, and reliability of AI models and their datasets is a critical concern (PointGuard AI Glossary).

How PointGuard AI Tackles AI Security Challenges

PointGuard AI addresses security risks associated with AI systems across their lifecycle—development, deployment, and runtime. Its approach involves:

• AI Discovery: Automatically inventories AI resources (models, datasets, notebooks) used across enterprises, creating visibility and managing supply chain risks, including “shadow AI”—projects outside central oversight.
• Security Testing & Red Teaming: Simulates attacks (red teaming, adversarial input, prompt injection, malware) and tests models for vulnerabilities, bias, and unsafe behaviors prior to deployment.
• Posture Management: Integrates with MLOps platforms (AWS, Azure, Databricks) to enforce access controls, check for misconfigurations, and maintain data integrity across the AI project lifecycle.
• Runtime Defense: Continuously monitors live AI for jailbreak attempts, prompt injections, policy violations, and data exfiltration attempts. It can block, mask, or redact sensitive information in real time to prevent leaks.
• Governance & Compliance: Provides centralized dashboards, automated remediation workflows, and governance features to help organizations meet regulatory, privacy, and ethical requirements (OWASP LLM/AI Security Glossary, NIST Glossary).

These capabilities enable PointGuard AI to secure not just standalone models but entire AI ecosystems, helping organizations innovate safely while ensuring compliance and reducing new risks from advanced AI.

Resources:

https://www.nasa.gov/what-is-artificial-intelligence/

https://cloud.google.com/learn/what-is-artificial-intelligence