Keeping Supported Software Secure: Advanced Security and AI Threat Intelligence

Why security in support is continuous, not a one-off

A penetration test or security audit gives you a snapshot: secure, as far as we can tell, on the day we looked. That is valuable, but it is not the same as staying secure.

Two forces erode a snapshot. First, the world moves: new vulnerabilities are disclosed every day, including in the frameworks, libraries, and runtimes your system depends on. A dependency that was safe when you shipped can carry a critical vulnerability a week later. Second, systems drift: a new subdomain gets exposed, a certificate lapses, a configuration changes, a secret is committed by accident.

Neither of these requires anyone on your team to do anything wrong. The system decays from secure to exposed on its own. Keeping software secure in support means running detection continuously and acting on what it finds, not booking an audit once a year.

The approach is defence in depth: several independent layers, so a gap in one is caught by another.

The layers of continuous security

GitHub Advanced Security: code, secrets, and dependencies

The first layer covers the code you own and the code you depend on, inside the workflow your developers already use.

• Code scanning with CodeQL analyses your source for vulnerability patterns such as injection flaws, unsafe deserialisation, and authentication mistakes. It runs on every change, so issues are caught before they reach production.
• Secret scanning catches credentials, tokens, and keys committed by mistake, and can block a push before the secret ever lands in history.
• Dependabot raises alerts when a dependency has a known vulnerability, and opens pull requests to update it. Keeping dependencies current is a security task as much as a maintenance one, covered in depth in our guide on keeping software up to date.

Together these give you continuous coverage of both first-party and third-party code, integrated with the GitHub Actions and Advanced Security pipeline rather than bolted on separately.

AI code analysis for context scanners miss

Rule-based scanners are precise but literal. They flag patterns, not intent, which produces two problems: false positives that waste time, and subtle flaws that no rule describes.

AI code analysis adds the missing context. It reads a finding alongside the surrounding code and can judge whether a flagged pattern is genuinely reachable and exploitable, or safe in this particular context. It can reason about logic flaws that pattern-matching cannot express, such as an authorisation check that is present but applied in the wrong order. And it explains findings in plain language with a drafted fix, which speeds up remediation. AI does not replace CodeQL; it makes the output of CodeQL and other scanners more actionable.

Detectify: the external attack surface

Code scanning looks at the system before it is deployed. An attacker looks at it after, from the outside. You need both views.

Detectify monitors your external attack surface: the domains, subdomains, and web applications reachable from the internet. It discovers assets you may have forgotten, tests running applications for vulnerabilities, and alerts you when something new or risky appears, such as a freshly exposed subdomain or a service running a vulnerable version. This is the view an attacker has, tested continuously rather than once.

Threat intelligence: knowing what to worry about

Not every vulnerability deserves the same urgency. A critical-rated flaw in a component you do not expose matters less than a medium-rated one that is being actively exploited in the wild against systems like yours.

Threat intelligence provides that judgement. Drawing on vendor advisories, the CISA Known Exploited Vulnerabilities catalogue, and CVE feeds, we learn about relevant vulnerabilities quickly, check whether your system is affected, and prioritise on real-world risk rather than a raw severity score read in isolation. The goal is to spend remediation effort where it reduces the most risk.

The continuous security process

These layers run as a continuous process. Detection flows outward from first-party code to dependencies, secrets, and the deployed attack surface, then is prioritised by live threat intelligence. Every finding converges on human review before any change reaches production.

How AI augments each layer

AI is not a separate layer; it makes the existing layers sharper.

• On code scanning, it reduces false positives and surfaces logic flaws that pattern rules cannot express, so engineers spend time on real issues.
• On dependencies, it assesses whether a flagged vulnerability is actually reachable in your usage of a library, which changes how urgent the update is.
• On attack surface, it correlates a newly exposed asset with what the code and infrastructure say should exist, flagging drift.
• On threat intelligence, it matches a new advisory against your specific stack in minutes, answering “are we affected?” faster than manual checking.

This is the same pattern as the rest of AI-augmented application support: AI does the volume and correlation work, and a person makes the security decision.

Responding to what the tools find

Detection is only half the job. The value is in a disciplined response.

1. Triage. Combine severity, exploitability, and exposure with threat intelligence to rank findings. A vulnerability being actively exploited against similar systems jumps the queue; a theoretical flaw in an unexposed path does not.
2. Remediate. Apply the fix: a dependency update, a code change, a configuration correction, or a compensating control. AI can draft the change, and Dependabot can raise the update, but a security-literate engineer reviews and tests it.
3. Verify. Confirm the fix closes the issue and does not regress the system, through the same quality process as any other change.
4. Report. Record what was found, what was done, and what remains, so security posture is visible rather than assumed.

Rushing this is how a security fix becomes a new incident. Automating detection while keeping remediation reviewed is the deliberate balance.

Security posture and compliance evidence

Continuous security also produces the evidence that governance and certification require. Scan results, dependency status, remediation records, and attack surface reports are a running account of how the system is kept secure. That supports frameworks such as ISO 27001 and Cyber Essentials, where demonstrating an ongoing process matters as much as any single control. The point-in-time audit becomes easier when the day-to-day process already generates the proof.

Where to start

1. Enable the basics. Turn on code scanning, secret scanning, and Dependabot alerts if they are not already active. They are the highest-value, lowest-effort starting point.
2. Map your external surface. List the domains, subdomains, and applications exposed to the internet. Most organisations find assets they had forgotten.
3. Get a security assessment. A maintainability review includes the system’s security posture and produces a prioritised remediation plan.

See our managed application support service for how continuous security fits into running your system, or book a consultation to discuss your security posture. For the maintenance side of staying secure, read our guide on keeping software up to date.