Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

With the increasing usage of AI systems in critical infrastructure and business operations, there is an inevitable need to secure these systems. AI pentesting is a domain-specific security assessment designed to identify and remediate vulnerabilities unique to AI systems, including machine learning models, training pipelines, and their underlying infrastructure.

Security testing hasn’t just fallen behind—it’s playing the wrong game in a world where product teams ship updates like software streams, testing once a year is akin to locking the doors after the party has ended. It’s not just late; it’s irrelevant. Most orgs still treat pentests like performance reviews: formal, infrequent, and disconnected from the day-to-day reality. But risk doesn’t work on an annual schedule.

Ask any CTO if they pentest their web apps, APIs, or cloud infrastructure; the answer is almost always yes. But ask if they’ve ever pentested their Salesforce environment, and you’ll likely get a silent—or hesitant- “Doesn’t Salesforce security cover that?” Here’s the problem: Salesforce is not just a CRM. It’s an application stack, a data warehouse, and a workflow engine—all deeply integrated with your business operations.

If you ask a security team if they run pentests on their web applications or APIs, the answer is always a strong “Yes”. But if you ask if they pentested their Umbraco setup, you will get a more hesitant, “I thought Umbraco is secure by default”. Umbraco is a powerful CMS, but assuming it is secure by default is a mistake.

Most teams approach network penetration testing the same way: pick a few well-known tools, run automated scans, and call it a day. But in today’s evolving threat landscape, that is a losing strategy. Attackers do not just rely on off-the-shelf exploits but adapt, chain vulnerabilities, and find gaps that automated tools miss. CTOs and engineering leaders need to rethink their approach with respect to context, strategy, and how they integrate into your security workflow.

The history of penetration testing begins with military strategies used to test enemy defenses. Over time, this evolved into a formal practice for identifying vulnerabilities in computer systems. This article traces the brief history of of penetration testing, from its early conceptual roots in military exercises, through the rise of ‘Tiger Teams’ in the 1970s, to the sophisticated tools and methodologies in use today.

When was the last time your business or enterprise tested its defenses with a real-world attack simulation? If the answer is never—or more than a year ago—your company may be more vulnerable than you think. Regular penetration testing by an expert team like Cybriant is one of the most effective ways to uncover and fix security weaknesses before attackers exploit them. Interested in learning more? Read on.

Imagine a bridge built without stress testing, where engineers only check for cracks after construction. When flaws inevitably appear, they scramble to patch weak spots until the subsequent failure forces another round of inspections. This is how most companies still approach pentesting: periodic assessments, reactive fixes, and security are treated as unwelcome checkpoints.

Most teams approach network penetration testing the same way: pick a few well-known tools, run automated scans, and call it a day. But in today’s evolving threat landscape, that is a losing strategy. Attackers do not just rely on off-the-shelf exploits but adapt, chain vulnerabilities, and find gaps that automated tools miss. CTOs and engineering leaders need to rethink their approach with respect to context, strategy, and how they integrate into your security workflow.

AI is reshaping industries, but security teams treat it like traditional software. Unfortunately, the real problem is AI models don’t just have bugs—they have systemic vulnerabilities. Adversarial manipulation, data poisoning, and model inversion aren’t edge cases; they’re real threats attackers are already exploiting. Yet, most security programs lack a structured approach to testing AI risks. Conventional pentesting isn’t enough.