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The frequency and sophistication of modern cyberattacks are surging, making it increasingly challenging for organizations to protect sensitive data and critical infrastructure. When attackers compromise a non-human identity (NHI), they can swiftly exploit it to move laterally across systems, identifying vulnerabilities and compromising additional NHIs in minutes. While organizations often take months to detect and contain such breaches, rapid detection and response can stop an attack in its tracks. The Rise of Non-Human Identities in Cybersecurity By 2025, non-human identities will rise to be the primary attack vector in cybersecurity. As businesses increasingly automate processes and adopt AI and IoT technologies, the number of NHIs grows exponentially. While these systems drive efficiency, they also create an expanded attack surface for cybercriminals. NHIs differ fundamentally from human users, making traditional security tools like multi-factor authentication and user behavior...

Behavioral analytics, long associated with threat detection (i.e. UEBA or UBA), is experiencing a renaissance. Once primarily used to identify suspicious activity, it's now being reimagined as a powerful post-detection technology that enhances incident response processes. By leveraging behavioral insights during alert triage and investigation, SOCs can transform their workflows to become more accurate, efficient, and impactful. Fortunately, many new cybersecurity products like AI SOC analysts are able to incorporate these techniques into their investigation capabilities, thus allowing SOCs to utilize them into their response processes. This post will provide a brief overview of behavior analytics then discuss 5 ways it's being reinvented to shake up SOC investigation and incident response work. Behavior Analysis is Back, But Why? Behavioral analytics was a hot topic back in 2015, promising to revolutionize static SIEM and SOC detections with dynamic anomaly detection to uncover t...

In recent years, artificial intelligence (AI) has begun revolutionizing Identity Access Management (IAM), reshaping how cybersecurity is approached in this crucial field. Leveraging AI in IAM is about tapping into its analytical capabilities to monitor access patterns and identify anomalies that could signal a potential security breach. The focus has expanded beyond merely managing human identities — now, autonomous systems, APIs, and connected devices also fall within the realm of AI-driven IAM, creating a dynamic security ecosystem that adapts and evolves in response to sophisticated cyber threats. The Role of AI and Machine Learning in IAM AI and machine learning (ML) are creating a more robust, proactive IAM system that continuously learns from the environment to enhance security. Let's explore how AI impacts key IAM components: Intelligent Monitoring and Anomaly Detection AI enables continuous monitoring of both human and non-human identities , including APIs, service acc...

Sherlock Holmes is famous for his incredible ability to sort through mounds of information; he removes the irrelevant and exposes the hidden truth. His philosophy is plain yet brilliant: "When you have eliminated the impossible, whatever remains, however improbable, must be the truth." Rather than following every lead, Holmes focuses on the details that are needed to move him to the solution. In cybersecurity, exposure validation mirrors Holmes' approach: Security teams are usually presented with an overwhelming list of vulnerabilities, yet not every vulnerability presents a real threat. Just as Holmes discards irrelevant clues, security teams must eliminate exposures that are unlikely to be exploited or do not pose significant risks. Exposure validation (sometimes called Adversarial Exposure Validation) enables teams to concentrate on the most significant issues and minimize distractions. Similar to Holmes' deductive reasoning, validation of exposures directs organizations towa...

The evolution of software always catches us by surprise. I remember betting against the IBM computer Deep Blue during its chess match against the grandmaster Garry Kasparov in 1997, only to be stunned when the machine claimed victory. Fast forward to today, would we have imagined just three years ago that a chatbot could write essays, handle customer support calls, and even craft commercial artwork? We continue to be amazed by what software can achieve—tasks we once thought were strictly human domains. Such is the surprise unfolding in the sphere of cybersecurity testing. Hold tight! Demystifying Penetration Testing If someone had told me 10 years ago that computer software could one day perform the work of an ethical hacker, I would have said 'No way, Jose'. Penetration testing—PT for short—is when experts mimic hackers to test a company's defenses. It's a critical practice, mandated by major regulatory bodies like PCI DSS, HIPAA, and DORA to ensure network safety. Yet, despite...

Security questionnaires aren't just an inconvenience — they're a recurring problem for security and sales teams. They bleed time from organizations, filling the schedules of professionals with monotonous, automatable work. But what if there were a way to reduce or even altogether eliminate security questionnaires? The root problem isn't a lack of great questionnaire products — it's the questionnaires themselves. At SafeBase, we don't just talk about transparency — it's core to everything we do, from how we build our products to how we communicate about them. In the spirit of transparency, in this piece we're going to talk about our Trust Center platform at length:  Why we're believers in Trust Centers > security questionnaires How a Trust Center reduces and eliminates questionnaires How to demonstrate the ROI of investing in a Trust Center Let's dive in. Why a trust center first approach helps Solving the questionnaire problem means going beyond the questionnaire wit...

The opportunities to use AI in workflow automation are many and varied, but one of the simplest ways to use AI to save time and enhance your organization's security posture is by building an automated SMS analysis service. Workflow automation platform Tines provides a good example of how to do it. The vendor recently released their first native AI features , and security teams have already started sharing the AI-enhanced workflows they've built using the platform.  Tines' library of pre-built workflows includes AI-enhanced pre-built workflows for normalizing alerts, creating cases, and determining which phishing emails require escalations.  Let's take a closer look at their SMS analysis workflow, which, like all of their pre-built workflows, is free to access and import, and can be used with a free Community Edition account.  Here, we'll share an overview of the workflow, and a step-by-step guide for getting it up and running. The problem - SMS scam messages targete...