Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Are We Creating an AI Security Nightmare?

Out tomorrow! 77% of enterprises have already faced AI-related security incidents. Are we building innovation—or just our next crisis? AI is becoming more prevalent, especially in the world of cybersecurity. However, there are many AI risks and dangers of AI that people should be aware of, including the weaponisation of ai. It's important to consider the ethical implications of artificial intelligence, especially in fields like ai in healthcare.

Ignite Creativity Using AI Image Generation Technology

In today's digital landscape, visual content has become paramount, with studies showing that posts with images receive 352% more engagement than those without. Yet, creating professional-quality visuals remains a significant challenge for many content creators, demanding substantial time, resources, and expertise. Innovative solutions like Kling AI are revolutionizing the way we create visual content. By harnessing the power of advanced artificial intelligence, creators can generate stunning, professional-grade images in minutes rather than hours.

How Device Intelligence Detects Fraud Without Using Personal Data

Fraud tactics now evolve on an hourly cycle. For banks, fintech, digital lenders, and payments players, the question isn't whether rules still help - it's whether they adapt fast enough. Recent numbers from Alloy's 2024 Financial Fraud Statistics underscore the shift: over 50% of surveyed institutions saw business fraud rise, two-thirds reported higher consumer fraud, and generative AI could drive $40B in bank losses by 2027. It's no surprise that more than half are raising third-party spend, with three in four prioritizing identity risk capabilities.

The Swiss Cheese Model of AI Security

The Swiss Cheese Model of AI Security A10 Networks' security experts, Jamison Utter, Madhav Aggarwal, and Diptanshu Purwar, explain that adequate AI security isn't a one-size-fits-all solution. They introduce the concept that security controls must be tailored to your specific data, company, and industry, as every context is unique.

Understanding Bias in Generative AI: Types, Causes & Consequences

Bias in generative AI refers to the systematic errors or distortions in the information produced by generative AI models, which can lead to unfair or discriminatory outcomes. These models, trained on vast datasets from the internet, often inherit and amplify the biases present in the data, mirroring societal prejudices and inequities.

Seven ways AI could impact the future of pen testing

In an era where attack surfaces are expanding faster than ever, AI has the potential to transform how organizations find and fix vulnerabilities. Gartner estimates AI agents will reduce the time it takes to exploit account vulnerabilities by 50%. From automating routine scans to developing self-learning attack agents, AI is already changing the red team playbook – and the pace of innovation shows no signs of slowing.

Shadow AI could be your organization's biggest threat.

What starts as innovation (an employee testing a new AI tool) can quickly become exposure. Unsanctioned apps create data leaks, compliance issues, and an expanded attack surface. With UpGuard User Risk, security teams gain visibility into shadow AI activity, so they can detect and neutralize risks before they escalate into breaches. activity before attackers can act. Ready to see what User Risk can do for you?