Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Generative AI is transforming the way enterprises approach daily operations – powering virtual assistants, summarizing medical records, and aiding clinicians with insights. These benefits come at a cost: risk to a wide range of sensitive data in AI-driven workflows. Traditional access controls and content filters that work for static systems fail as these are not designed for the free-flowing, context-rich data exchanges in LLM applications.

Personally Identifiable Information (PII) is scattered across cloud storage, emails, and databases—making it a prime target for breaches and compliance failures. Automated PII detection, powered by rule-based and machine learning models, helps organizations discover, classify, and secure sensitive information in real time. This guide breaks down how detection works across structured and unstructured data, modern redaction techniques, model customization, and integration with Netwrix DSPM.

AI-powered social engineering attacks are significantly more successful than traditional attacks, according to a new report from cyber risk management firm Resilience. The researchers state, “Social engineering attacks fueled 88% of material losses, with AI-powered phishing achieving a 54% success rate compared to just 12% for traditional attempts.” AI allows attackers to easily craft sophisticated phishing emails, as well as voice and video deepfakes.

CMMC is a strict certification, but there’s also a lot of variation within its security controls and the demands it makes of agencies looking to achieve that certification. The standards are high, especially at the higher levels of CMMC, but there are also many tools and platforms available to meet those needs appropriately, without reinventing the wheel from base principles. Businesses need the tools necessary to function in a modern digital world.

Regulators aren’t just cracking down on digital fraud – they’re rewriting the rules on who’s responsible when it happens. Across every major region, laws are shifting liability closer to the first point of compromise: the login session. If your digital environment can’t detect a spoofed page, stop a phishing attempt, or block credential theft in real time, you’re not just at risk – you may be out of compliance.

AI has become essential infrastructure for modern business. What started as pilot programs has evolved into production deployments across business functions, fundamentally changing how work gets done. While this transformation drives significant productivity gains, it creates a fundamental security challenge that traditional data loss prevention (DLP) approaches can't address.

At Syteca, we don’t build features for the sake of it — we build them to solve real problems. That’s why we’re proud to launch Syteca’s newest release: a meaningful upgrade built around the needs of our clients. From a fresh visual design and data masking to agentless access and enhanced session playback, this update addresses the real needs of IT teams for better data privacy, simplified access management, and seamless oversight.

Ransomware is evolving ‒ not fading. Despite a decline in attack detections based on WatchGuard Firebox telemetry, data from extortion sites and media reporting tells a different story: ransomware activity is actually on the rise, both quarter-over-quarter and year-over-year. The number of active ransomware groups is also increasing, as is the average ransom demand. In fact, the typical payout jumped from $400,000 in 2023 to $2 million in 2024 ‒ a staggering 500% spike.

Identity-based attacks are on the rise. Attacks in which malicious actors assume the identity of an entity to easily gain access to resources and sensitive data have been increasing in number and frequency over the last few years.

AI agents powered by Amazon Bedrock are playing an increasingly central role in cloud operations. These agents can interact directly with core AWS services like S3, Lambda, RDS, and EC2 to perform tasks such as data retrieval, automation orchestration, and resource provisioning. Many teams rely on the Model Context Protocol (MCP) to structure agent behavior and convert natural language into actionable commands.