Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.

Attackers inside OT environments don’t run, they walk slowly, blend in, and map everything before they act. Here’s why cyber deception technology is built for exactly that threat.

Organisations deploying agents face a challenge: the predominant AI frameworks most organisations rely on do not explicitly address agentic AI. This is true for the big three: ISO 42001, NIST's AI Risk Management Framework (RMF), and the EU AI Act.

Cisco polled its major enterprise customers before RSA 2026 and found something astounding. 85% of large enterprises are experimenting with AI agents. Only 5% have moved them into production. That's not a technology gap. The models work. The tools exist. The 80-point spread between experimentation and production is a governance gap. It's also a context gap.

Organizations have traditionally treated phishing emails as a technology problem to be solved with spam filters and secure email gateways. But with phishing attacks on the rise, these tactics are no longer enough. KnowBe4’s 2025 Phishing By Industry Benchmarking Report found a 47% increase in phishing attacks that bypass Microsoft’s native defenses and secure email gateways. Why do they succeed? Because they exploit reliable human behavior.

OT Data Protection & Resilience is the practice of securing industrial control system data, such as PLC logic, HMI configurations, and historian archives, against loss or cyberattack, while ensuring fast recovery to maintain safety and production continuity. In modern industrial environments, the traditional concept of"security as a barrier" is no longer sufficient.

The first time I helped support a control server recovery at a packaging facility, someone asked "How long will this take?" We did not have a good answer. The backup existed. The process to restore it safely in a live production environment did not. We had carried IT assumptions into an OT problem, and it cost the plant an entire shift. IT/OT convergence created a gap that most backup vendors were not built to close. Production systems need continuous uptime.

As cyberattacks on industrial systems escalate and supply chains grow more complex, cyber resilience for operational technology (OT) isn’t optional — it’s a business imperative. But with budgets under pressure and resources stretched thin, many manufacturers are left pondering a difficult question: How can we strengthen OT defenses without incurring downtime or breaking the bank? Virtual conference OT resilience.

Note: The core architecture for this pattern was introduced by Isaac Hawley from Tigera. If you are building an AI agent that relies on tool calling, complex routing, or the Model Context Protocol (MCP), you’re not just building a chatbot anymore. You are building an autonomous system with access to your internal APIs. With that power comes a massive security and governance headache, and AI agent security testing is where most teams hit a wall.

Another SaaS integration for the books! Incidents can now be automatically generated in the portal from email addresses in ProofPoint alerts. These are then linked to portal users, as well as any devices those users are logged in to.