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The U.K. and U.S., along with international partners from 16 other countries, have released new guidelines for the development of secure artificial intelligence (AI) systems. "The approach prioritizes ownership of security outcomes for customers, embraces radical transparency and accountability, and establishes organizational structures where secure design is a top priority," the U.S. Cybersecurity and Infrastructure Security Agency (CISA)  said . The goal is to  increase cyber security levels of AI  and help ensure that the technology is designed, developed, and deployed in a secure manner, the National Cyber Security Centre (NCSC)  added . The guidelines also build upon the U.S. government's  ongoing   efforts  to manage the risks posed by AI by ensuring that new tools are tested adequately before public release, there are guardrails in place to address societal harms, such as bias and discrimination, and privacy concerns, and setting up robust methods for consumer

Here is what matters most when it comes to artificial intelligence (AI) in cybersecurity: Outcomes.  As the threat landscape evolves and  generative AI is added  to the toolsets available to defenders and attackers alike, evaluating the relative effectiveness of various  AI-based security  offerings is increasingly important — and difficult. Asking the right questions can help you spot solutions that deliver value and ROI, instead of just marketing hype. Questions like, "Can your predictive AI tools sufficiently block what's new?" and, "What actually signals success in a cybersecurity platform powered by artificial intelligence?" As BlackBerry's AI and ML (machine learning) patent portfolio attests, BlackBerry is a leader in this space and has developed an exceptionally well-informed point of view on what works and why. Let's explore this timely topic. Evolution of AI in Cybersecurity Some of the earliest uses of ML and AI in cybersecurity date back to the de

Ambitious Employees Tout New AI Tools, Ignore Serious SaaS Security Risks Like the  SaaS shadow IT  of the past, AI is placing CISOs and cybersecurity teams in a tough but familiar spot.  Employees are covertly using AI  with little regard for established IT and cybersecurity review procedures. Considering  ChatGPT's meteoric rise to 100 million users within 60 days of launch , especially with little sales and marketing fanfare, employee-driven demand for AI tools will only escalate.  As new studies show  some workers boost productivity by 40% using generative AI , the pressure for CISOs and their teams to fast-track AI adoption — and turn a blind eye to unsanctioned AI tool usage — is intensifying.  But succumbing to these pressures can introduce serious SaaS data leakage and breach risks, particularly as employees flock to AI tools developed by small businesses, solopreneurs, and indie developers. AI Security Guide Download AppOmni's CISO Guide to AI Security - Part 1 AI evoke

Malicious ads served inside Microsoft Bing's artificial intelligence (AI) chatbot are being used to distribute malware when searching for popular tools. The findings come from Malwarebytes, which revealed that unsuspecting users can be tricked into visiting booby-trapped sites and installing malware directly from Bing Chat conversations. Introduced by Microsoft in February 2023, Bing Chat is an  interactive search experience  that's powered by OpenAI's large language model called  GPT-4 . A month later, the tech giant  began   exploring  placing ads in the conversations. But the move has also opened the doors for threat actors who resort to malvertising tactics and propagate malware. "Ads can be inserted into a Bing Chat conversation in various ways," Jérôme Segura, director of threat intelligence at Malwarebytes,  said . "One of those is when a user hovers over a link and an ad is displayed first before the organic result." In an example highligh

A novel cloud-native cryptojacking operation has set its eyes on uncommon Amazon Web Services (AWS) offerings such as AWS Amplify, AWS Fargate, and Amazon SageMaker to illicitly mine cryptocurrency. The malicious cyber activity has been codenamed  AMBERSQUID  by cloud and container security firm Sysdig. "The AMBERSQUID operation was able to exploit cloud services without triggering the AWS requirement for approval of more resources, as would be the case if they only spammed EC2 instances," Sysdig security researcher Alessandro Brucato said in a report shared with The Hacker News. "Targeting multiple services also poses additional challenges, like incident response, since it requires finding and killing all miners in each exploited service." Sysdig said it discovered the campaign following an  analysis of 1.7 million images  on Docker Hub, attributing it with moderate confidence to Indonesian attackers based on the use of Indonesian language in scripts and use

There's been a great deal of AI hype recently, but that doesn't mean the robots are here to replace us. This article sets the record straight and explains how businesses should approach AI. From musing about self-driving cars to fearing AI bots that could destroy the world, there has been a great deal of AI hype in the past few years. AI has captured our imaginations, dreams, and occasionally, our nightmares. However, the reality is that AI is currently much less advanced than we anticipated it would be by now. Autonomous cars, for example, often considered the poster child of AI's limitless future, represent a narrow use case and are not yet a common application across all transportation sectors. In this article, we de-hype AI, provide tools for businesses approaching AI and share information to help stakeholders educate themselves.  AI Terminology De-Hyped AI vs. ML AI (Artificial Intelligence) and ML (Machine Learning) are terms that are often used interchangeably, but the

In today's digital landscape, your business data is more than just numbers—it's a powerhouse. Imagine leveraging this data not only for profit but also for enhanced AI and Machine Learning (ML) threat detection. For companies like Comcast, this isn't a dream. It's reality. Your business comprehends its risks, vulnerabilities, and the unique environment in which it operates. No generic, one-size-fits-all tool can capture this nuance. By utilizing your own data, you position yourself ahead of potential threats, enabling informed decisions and safeguarding your assets. Join our groundbreaking webinar, " Clean Data, Better Detections: Using Your Business Data for AI/ML Detections ," to unearth how your distinct business data can be the linchpin to amplifying your AI/ML threat detection prowess. This webinar will endow you with the insights and tools necessary to harness your business data, leading to sharper, more efficient, and potent threat detections. UPC

While IT security managers in companies and public administrations rely on the concept of Zero Trust, APTS (Advanced Persistent Threats) are putting its practical effectiveness to the test. Analysts, on the other hand, understand that Zero Trust can only be achieved with comprehensive insight into one's own network.  Just recently, an attack believed to be perpetrated by the Chinese hacker group  Storm-0558  targeted several government agencies. They used fake digital authentication tokens to access webmail accounts running on Microsoft's Outlook service. In this incident, the attackers stole a signing key from Microsoft, enabling them to issue functional access tokens for Outlook Web Access (OWA) and Outlook.com and to download emails and attachments. Due to a plausibility check error, the digital signature, which was only intended for private customer accounts (MSA), also worked in the Azure Active Directory for business customers.  Embracing the Zero Trust Revolution Acc

Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table. Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity a

The Italian data protection watchdog, Garante per la Protezione dei Dati Personali (aka Garante), has imposed a temporary ban of OpenAI's ChatGPT service in the country, citing data protection concerns. To that end, it has ordered the company to stop processing users' data with immediate effect, stating it intends to investigate the company over whether it's unlawfully processing such data in violation of the E.U. General Data Protection Regulation ( GDPR ) laws. "No information is provided to users and data subjects whose data are collected by Open AI," the Garante  noted . "More importantly, there appears to be no legal basis underpinning the massive collection and processing of personal data in order to 'train' the algorithms on which the platform relies." ChatGPT, which is estimated to have reached over 100 million monthly active users since its release late last year,  has not   disclosed   what it used  to train its latest large languag

Microsoft on Tuesday  unveiled   Security Copilot  in limited preview, marking its continued quest to embed AI-oriented features in an attempt to offer "end-to-end defense at machine speed and scale." Powered by OpenAI's GPT-4 generative AI and its own security-specific model, it's billed as a  security analysis tool  that enables cybersecurity analysts to quickly respond to threats, process signals, and assess risk exposure. To that end, it collates insights and data from various products like Microsoft Sentinel, Defender, and Intune to help security teams better understand their environment; determine if they are susceptible to known vulnerabilities and exploits; identify ongoing attacks, their scale, and receive remediation instructions; and summarize incidents. Users, for instance, can ask Security Copilot about suspicious user logins over a specific time period, or even employ it to create a PowerPoint presentation outlining an incident and its attack chain.

As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called "needle in the haystack". With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad. The Role of ML-Driven Network Security Solutions

The maintainers of the PyTorch package have warned users who have installed the nightly builds of the library between December 25, 2022, and December 30, 2022, to uninstall and download the latest versions following a  dependency confusion attack . "PyTorch-nightly Linux packages installed via pip during that time installed a dependency,  torchtriton , which was compromised on the Python Package Index (PyPI) code repository and ran a malicious binary," the PyTorch team  said  in an alert over the weekend. PyTorch, analogous to Keras and TensorFlow, is an open source Python-based machine learning framework that was originally developed by Meta Platforms. The PyTorch team said that it became aware of the malicious dependency on December 30, 4:40 p.m. GMT. The supply chain attack entailed uploading the malware-laced copy of a legitimate dependency named torchtriton to the Python Package Index (PyPI) code repository. Since package managers like pip check public code registr