Every term. Explained properly.

Deciding which vulnerabilities can actually be exploited in your code — and ignoring the rest.

Tracing untrusted input from source to sink to find exploitable data flows.

Finding novel vulnerabilities before they are publicly disclosed.

A map of which functions call which — the foundation of reachability and taint analysis.

The four vulnerability intelligence signals you actually need to prioritise work.

Finding the known vulnerabilities in the open-source components your software depends on.

Analysing source code for vulnerabilities without running it.

Testing a running application from the outside, the way an attacker would.

Finding credentials that have been committed to source, images, or logs.

Pre-configured rules that stop risky changes before they merge.

The same rule set applied at PR time, build time, admission, and runtime.

The Kubernetes hook that rejects non-compliant workloads before they schedule.

An audited bypass for legitimately urgent situations that would otherwise be blocked.

Catching configuration that has quietly moved away from its declared state.

An auto-generated pull request that remediates a specific vulnerability.

Safeguard's reasoning engine — turns engine output into human-reviewable findings and fix PRs.

A server that exposes tools to AI agents via the Model Context Protocol.

Governance, signing, scoped credentials, and audit logs for MCP servers.

Giving each LLM exactly the tools it needs, with bounded privilege and audit.

Restricting what each MCP server or AI tool can actually do — even if the model asks for more.

The attack class where untrusted text in a model's context becomes a command it follows.

A regression test suite for AI workflow quality, gated into release.

Matching the right model family to the right security task for cost and quality.

Training a smaller, cheaper model to reproduce the reasoning of a larger one on a narrow task.

The curated body of vulnerabilities, patches and exploit write-ups a security model is trained on.

Hiding instructions in an MCP tool description so the model follows them without the user ever seeing.

Continuous inventory and risk assessment of every model, agent and AI service in the estate.

Models, agents and AI tools in use across an organisation that no one has approved or inventoried.

Giving an AI agent its own credential and audit trail instead of letting it borrow a human's.