US SEC, SolarWinds reach preliminary deal to end breach lawsuit
The SEC, SolarWinds and its chief information security officer, Timothy Brown, asked a federal judge on Wednesday to stay court proceedings while they finalize paperwork for a settlement. The judge granted their motion, filings showed.
In what was seen as a landmark case, the SEC sued the software company and its top security executive in connection with a two-year cyberattack known as Sunburst that targeted Austin, Texas-based SolarWinds. A judge dismissed much of the regulator's case last year.
The SEC had said that the defendants defrauded investors by concealing security weaknesses, but U.S. District Judge Paul Engelmayer, who approved the stay, had said that the claims were based on "hindsight and speculation."
An SEC spokesperson declined to comment on the matter beyond the public filings. SolarWinds did not immediately respond to requests for comment.
The parties said they planned to file settlement paperwork or a joint status report by September 12.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Daily Mail
20 minutes ago
- Daily Mail
Bizarre creature with elongated head caught on doorbell camera sparks fears of visitors from another planet
A frightening sight was captured on a woman's doorbell camera which appeared show an extraterrestrial walking through her backyard. Jessica Ortiz, from Compton, California, revealed the June 5 clip on her Instagram account, showing a bizarre creature walking in her driveway and exiting through a back gate. This short, blue ' alien ' had an elongated head, a tiny neck that seemed to be coming out of the creature's chest, and a protruding backbone. As the creature moves out of view, you can hear Ortiz's gate slam shut after the invader leaves her property. Ortiz recently revealed that the incident took place around 1am PT and was only captured by her front door camera, even though she had motion-activated cameras all around her home. 'That's why we know for sure it's not a person because there's only one way in and there's only one way out,' Ortiz said while sharing her story with the YouTube channel The Hidden Underbelly 2.0. The scary incident got even more bizarre after Ortiz's aunt, who is also her next-door neighbor, said she heard banging and scratching noises on her roof just 30 minutes before the creature was caught on camera. As for why her other cameras didn't detect the suspected alien invader, Ortiz shared that the video seemed to show the creature materializing out of nowhere right before walking past the front door. Ortiz added that the encounter started when she received an alert on her Ring doorbell app. 'I asked my son to look at the video. I brought the phone and I was like 'Hey, double check this for me, what am I seeing?' and he saw the video like three times and he's like 'Mom it's an alien, call the cops.'' she explained during the June 27 interview. Despite her son's suggestion and her own fears about what was lurking in their neighborhood, Ortiz never ended up calling the police. 'It was scary. At that moment, I was like, what do I do?' the mother said. 'I was like 'we're going to sound crazy calling the police saying there's an alien in the backyard,'' Ortiz continued. Although Ortiz wouldn't call the strange sighting an alien, she said she does believe we're not the only living beings in this universe. The host of The Hidden Underbelly podcast, who did not give his name, speculated that the creature was indeed an alien and appeared to be from a race UFO researchers refer to as 'the Greys.' He noted that alleged alien encounters over the years have cited two distinct species of Greys, one extremely tall race and another short and strangely shaped race. This 'alien,' which appeared to be no more than three or four feet in height, seemed to fit the description of the short Greys. Although both Ortiz and her teenage son were still awake that night, the YouTube host pointed out that it seemed very unlikely a small children would have been out roaming the streets at 1am. 'Look at its head, it's a very weird shape,' the host explained. 'And the color, this is looking like a blue-grey creature, of the typical Greys.' 'Could this be somebody in an elaborate costume? This would have to be a very small person,' he said. In October 2023, Ring doorbell cameras, an Amazon-owned company, offered a $1 million grand prize to any US resident who provided 'unaltered scientific evidence of a real extraterrestrial lifeform.' The month-long contest came after troves of videos claiming to show extraterrestrial activity flooded the internet. However, most of the sightings turned out to be meteors falling from the sky. No one ended up winning the 2023 contest by Ring. Ortiz was asked about Ring and Amazon's offer and said she would enter it if the companies are still taking submissions, adding that her video was not edited in any way.
Reuters
32 minutes ago
- Reuters
Ingram Micro says identified ransomware on certain of its internal systems
July 5 (Reuters) - Ingram Micro (INGM.N), opens new tab said on Saturday it recently identified ransomware on certain of its internal systems. The information technology company took steps to secure the relevant environment, including taking certain systems offline, it said in a statement. The Irvine, California-based company also launched an investigation with the assistance of leading cybersecurity experts and notified law enforcement, it added.
Finextra
an hour ago
- Finextra
Context Engineering for Financial Services: By Steve Wilcockson
The hottest discussion in AI right now, at least the one not about Agentic AI, is about how "context engineering" is more important than prompt engineering, how you give AI the data and information it needs to make decisions, and it cannot (and must not) be a solely technical function. "'Context' is actually how your company operates; the ideal versions of your reports, documents & processes that the AI can use as a model; the tone & voice of your organization. It is a cross-functional problem.' So says renowned Tech Influencer and Associate Professor at Wharton School, Ethan Molick. He in turn cites fellow Tech Influencer Andrej Karpathy on X, who in turn cites Tobi Lutke, CEO of Shopify: "It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. " The three together - Molick, Karpathy and Lutke - make for a powerful triumvirate of Tech-influencers. Karpathy consolidates the subject nicely. He emphasizes that in real-world, industrial-strength LLM applications, the challenge entails filling the model's context window with just the right mix of information. He thinks about context engineering as both a science—because it involves structured systems and system-level thinking, data pipelines, and optimization —and an art, because it requires intuition about how LLMs interpret and prioritize information. His analysis reflects two of my predictions for 2025 one highlighting the increasing impact of uncertainty and another a growing appreciation of knowledge. Tech mortals offered further useful comments on the threads, two of my favorites being: 'Owning knowledge no longer sets anyone apart; what matters is pattern literacy—the ability to frame a goal, spot exactly what you don't know, and pull in just the right strands of information while an AI loom weaves those strands into coherent solutions.' weaves those strands into coherent solutions.' 'It also feels like 'leadership' Tobi. How to give enough information, goal and then empower.' I love the AI loom analogy, in part because it corresponds with one of my favorite data descriptors, the "Contextual Fabric". I like the leadership positivity too, because the AI looms and contextual fabrics, are led by and empowered by humanity. Here's my spin, to take or leave. Knowledge, based on data, isn't singular, it's contingent, contextual. Knowledge and thus the contextual fabric of data on which it is embedded is ever changing, constantly shifting, dependent on situations and needs. I believe knowledge is shaped by who speaks, who listens, and what about. That is, to a large extent, led by power and the powerful. Whether in Latin, science, religious education, finance and now AI, what counts as 'truth' is often a function of who gets to tell the story. It's not just about what you know, but how, why, and where you know it, and who told you it. But of course it's not that simple; agency matters - the peasant can become an abbot, the council house schoolgirl can become a Nobel prize-winning scientist, a frontier barbarian can become a Roman emperor. For AI, truth to power is held by the big tech firms and grounded on bias, but on the other it's democratizing in that all of us and our experiences help train and ground AI, in theory at least. I digress. For AI-informed decision intelligence, context will likely be the new computation that makes GenAI tooling more useful than simply being an oft-hallucinating stochastic parrot, while enhancing traditional AI - predictive machine learning, for example - to be increasingly relevant and affordable for the enterprise. Context Engineering for FinTech Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical discipline in tech. This is like gold for those of us in the FinTech data engineering space, because we're the dudes helping you create your own context. I'll explore how five different contextual approaches, all representing data engineering-relevant vendors I have worked for —technical computing, vector-based, time-series, graph and geospatial platforms—can support context engineering. Parameterizing with Technical Computing Technical computing tools – think R, Julia, MATLAB and Python's SciPy stack - can integrate domain-specific data directly into the model's environment through structured inputs, simulations, and real-time sensor data, normally as vectors, tables or matrices. For example, in engineering or robotics applications, an AI model can be fed with contextual information such as system dynamics, environmental parameters, or control constraints. Thus the model can make decisions that are not just statistically sound but also physically meaningful within the modeled system. They can dynamically update the context window of an AI model, for example in scenarios like predictive maintenance or adaptive control, where AI must continuously adapt to new data. By embedding contextual cues, like historical trends, operational thresholds, or user-defined rules, such tools help ground the model's outputs in the specific realities of the task or domain. Financial Services Use Cases Quantitative Strategy Simulation Simulate trading strategies and feed results into an LLM for interpretation or optimization. Stress Testing Financial Models Run Monte Carlo simulations or scenario analyses and use the outputs to inform LLMs about potential systemic risks. Vectors and the Semantics of Similarity Vector embeddings are closely related to the linear algebra of technical computing, but they bring semantic context to the table. Typically stored in so-called vector databases, they encode meaning into high-dimensional space, allowing AI to retrieve through search not just exact matches, but conceptual neighbors. They thus allow for multiple stochastically arranged answers, not just one. Until recently, vector embeddings and vector databases have been primary providers of enterprise context to LLMs, shoehorning all types of data as searchable mathematical vectors. Their downside is their brute force and compute-intensive approach to storing and searching data. That said, they use similar transfer learning approaches – and deep neural nets – to those that drive LLMs. As expensive, powerful brute force vehicles of Retrieval-Augmented Generation (RAG), vector databases don't simply just store documents but understand them, and have an increasingly proven place for enabling LLMs to ground their outputs in relevant, contextualized knowledge. Financial Services Use Cases Customer Support Automation Retrieve similar past queries, regulatory documents, or product FAQs to inform LLM responses in real-time. Fraud Pattern Matching Embed transaction descriptions and retrieve similar fraud cases to help the model assess risk or flag suspicious behavior. Time-Series, Temporal and Streaming Context Time-series database and analytics providers, and in-memory and columnar databases that can organize their data structures by time, specialize in knowing about the when. They can ensure temporal context—the heartbeat of many use cases in financial markets as well as IoT, and edge computing- grounds AI at the right time with time-denominated sequential accuracy. Streaming systems, like Kafka, Flink, et al can also facilitate the real-time central nervous systems of financial event-based systems. It's not just about having access to time-stamped data, but analyzing it in motion, enabling AI to detect patterns, anomalies, and causality, as close as possible to real time. In context engineering, this is gold. Whether it's fraud that happens in milliseconds or sensor data populating insurance telematics, temporal granularity can be the difference between insight and noise, with context stored and delivered by what some might see as a data timehouse. Financial Services Use Cases Market Anomaly Detection Injecting real-time price, volume, and volatility data into an LLM's context allows it to detect and explain unusual market behavior. High-Frequency Trading Insights Feed LLMs with microsecond-level trade data to analyze execution quality or latency arbitrage. Graphs That Know Who's Who Graph and relationship-focussed providers play a powerful role in context engineering by structuring and surfacing relationships between entities that are otherwise hidden in raw data. In the context of large language models (LLMs), graph platforms can dynamically populate the model's context window with relevant, interconnected knowledge—such as relationships between people, organizations, events, or transactions. They enable the model to reason more effectively, disambiguate entities, and generate responses that are grounded in a rich, structured understanding of the domain. Graphs can act as a contextual memory layer through GraphRAG and Contextual RAG, ensuring that the LLM operates with awareness of the most relevant and trustworthy information. For example, graph databases - or other environments, e.g. Spark, that can store graph data types as accessible files, e.g. Parquet, HDFS - can be used to retrieve a subgraph of relevant nodes and edges based on a user query, which can then be serialized into natural language or structured prompts for the LLM. Platforms that focus graph context around entity resolution and contextual decision intelligence can enrich the model's context with high-confidence, real-world connections—especially useful in domains like fraud detection, anti-money laundering, or customer intelligence. Think of them as like Shakespeare's Comedy of Errors meets Netflix's Department Q. Two Antipholuses and two Dromios rather than 1 of each in Comedy of Errors? Only 1 Jennings brother to investigate in Department Q's case, and where does Kelly MacDonald fit into anything? Entity resolution and graph context can help resolve and connect them in a way that more standard data repositories and analytics tools struggle with. LLMs cannot function without correct and contingent knowledge of people, places, things and the relationships between them, though to be sure many types of AI can also help discover the connections and resolve entities in the first place. Financial Services Use Cases AML and KYC Investigations Surface hidden connections between accounts, transactions, and entities to inform LLMs during risk assessments. Credit Risk Analysis Use relationship graphs to understand borrower affiliations, guarantors, and exposure networks. Seeing the World in Geospatial Layers Geospatial platforms support context engineering by embedding spatial awareness into AI systems, enabling them to reason about location, proximity, movement, and environmental context. They can provide rich, structured data layers (e.g., terrain, infrastructure, demographics, weather) that can be dynamically retrieved and injected into an LLM's context window. This allows the model to generate responses that are not only linguistically coherent but also geographically grounded. For example, in disaster response, a geospatial platform can provide real-time satellite imagery, flood zones, and population density maps. This data can be translated into structured prompts or visual inputs for an AI model tasked with coordinating relief efforts or summarizing risk. Similarly, in urban planning or logistics, geospatial context helps the model understand constraints like traffic patterns, zoning laws, or accessibility. In essence, geospatial platforms act as a spatial memory layer, enriching the model's understanding of the physical world and enabling more accurate, context-aware decision-making. Financial Services Use Cases Branch Network Optimization Combine demographic, economic, and competitor data to help LLMs recommend new branch locations. Climate Risk Assessment Integrate flood zones, wildfire risk, or urban heat maps to evaluate the environmental exposure of mortgage and insurance portfolios. Context Engineering Beyond the Limits of Data, Knowledge & Truths Context engineering I believe recognizes that data is partial, and that knowledge and perhaps truth or truths needs to be situated, connected, and interpreted. Whether through graphs, time-series, vectors, tech computing platforms, or geospatial layering, AI depends on weaving the right contextual strands together. Where AI represents the loom, the five types of platforms I describe are like the spindles, needles, and dyes drawing on their respective contextual fabrics of ever changing data, driving threads of knowledge—contingent, contextual, and ready for action.
