Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Artificial Intelligence has quickly become one of the most important conversations in cybersecurity. Much of that conversation focuses on what attackers might create next: AI-generated malware, deepfakes, autonomous attacks, or entirely new categories of threats. Those risks matter, but focusing only on new attack techniques misses a much larger transformation already taking place. The real impact of AI is not only what attackers can create. It is how efficiently they can operate.

Join us at swampUP New York, September 1-3, for our joint session Trusted AI Delivery at Scale: Securing Every Artifact from Curation to Cloud, where we walk the full chain of custody from the moment a package enters your organization to the moment your agent runs on Amazon Bedrock AgentCore. Register here. AI agents are becoming real users of internal systems. They open pull requests, run queries, and pull and publish artifacts in repositories like JFrog Artifactory.

As generative AI adoption surges, so does a dangerous new enterprise risk: AI prompt data leakage — the unintentional exposure of confidential corporate data to third-party Large Language Models via user prompts. Why does it happen? Driven by productivity pressure and the need to speed up their work, employees routinely bypass traditional DLP controls.

Two teams inventory the same organization and return different numbers. One counts forty-one AI assets, the other counts one hundred and twelve. Neither is wrong, because they counted different things, and nobody had decided what a row represents. ‍ Guidance on building an AI inventory covers which fields a row should carry and skips what a row is. That question determines the count, the risk scores, the regulatory classification and whether two inventories can ever be reconciled.

Ask any security engineer what they actually think about their vulnerability scanner, and you will get a version of the same answer. They trust maybe 20% of what shows up in the patching queue. The rest gets a suspicious glance, and a slow death in a backlog. That is the real cost of a false positive. It is quiet, it compounds, and it hollows the tool out from the inside. It is also the reason autonomous pentesting came to replace hypotheses with confirmed exploits.

Adversaries are using AI to launch cyber attacks in record time, forcing security teams to measure responses in minutes instead of months. Phishing campaigns built with large language models (LLMs) achieve click-through rates 4.5 times higher than traditional methods,1 and the average time between initial compromise and lateral movement has fallen to just 29 minutes.2 This is a 65% increase from the prior year.2 State and local governments and higher education institutions are at an inflection point.

Gartner predicted in mid-2025 that more than forty percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Treat the figure as a forward-looking estimate rather than a measurement, since canceled projects tend to be quietly renamed, absorbed or left to lapse rather than formally closed. ‍

AI coding tools help developers build and deploy LLM applications quickly, but this speed comes with new security risks. Traditional static application security testing (SAST) tools that are pattern based weren’t designed to detect LLM-specific issues such as prompt injection sinks and exposed system prompts. These vulnerabilities often don’t become apparent until applications are already running in production, when remediation is more difficult and expensive.

Security and engineering teams contend with a constant stream of signals about vulnerabilities, incidents, misconfigurations, identity risks, control gaps, and other findings across their environments. But an individual finding’s severity does not always reflect its potential organizational impact.