Headlines
An agentic loop is an iterative cycle of execution in which an AI agent receives tasks or objective, prepares or determines the next course of action, carries it out, evaluates the results, determines if the task is finished, and either terminate.
A complete strategy for preserving the security and integrity of machine learning (ML) and artificial intelligence (AI) systems is called AI security posture management, or AI-SPM.
China is likely to use artificial intelligence to influence Taiwan’s Nov. 28 local elections.Taiwan’s ruling Democratic Progressive Party, or DPP, warned this month that Beijing could use AI to target groups of voters, impersonate Taiwanese social media users and circulate fabricated recordings or scandals shortly before election day.
The hypothetical intelligence of a machine that can comprehend or learn any intellectual task that a human being can is known as artificial general intelligence (AGI).
A subfield of machine learning called reinforcement learning (RL) focuses on how agents might learn to maximize cumulative rewards by making decisions via trial and error.
A comprehensive approach for preventing unwanted access, disclosure, alteration, or destruction of an organization’s sensitive data is Data security posture management (DSPM).
The practice of adversarial testing designed specifically for AI systems to find weaknesses, security holes, and safety concerns before attackers take advantage of them is known as “AI red teaming.”
The process for identifying data points in a dataset or system that deviate from the norm is known as anomaly detection.Anomaly detection systems can swiftly detect anomalous patterns or points that may indicate a potential threat or an ongoing cyberattack by using machine learning (ML) and artificial intelligence (AI) to identify deviations from normal behavior and events within a network or system.
When a model generates inaccurate, incorrect, or misleading outputs that are not supported by the input or training data, this is known as an AI hallucination. This usually occurs when the model identifies patterns improperly, producing unreliable or bias results.
AI threat detection uses automation, behavioral analytics, and machine learning to instantly detect cyber threats.The use of AI technologies, including as machine learning models, large language models (LLMs), and natural language processing (NLP), to augment and expedite up threat identification, investigation, and mitigation in real time is known as AI-driven threat detection and response.