If only hackers were the biggest security issue you have to worry about.
When movies and TV shows portray cybercriminals breaking into large enterprises, it’s dramatic, with pulsating background music as data is stolen and misused.
The everyday reality is a lot different.
The culprits aren’t bad actors. They’re developers who failed to code software securely.
They didn’t conduct a proper scan and weren’t aware of the latest AI-powered brute-force attacks, vulnerabilities, and threats.
Software security problems don’t look like problems until something goes wrong. Very wrong.
The best defense goes beyond technology. It’s a combination of learning, preparing, and making software security a top business priority.
Think like a CISO by:
1. Upskilling your ability to fortify high-stakes software installations
We’re all just one misconfiguration or exploit away from potential disaster.
Trying to learn security best practices on the fly is not going to work. Attack vectors and vulnerabilities change too quickly for that.
Implement access control, keep components updated, and more in our Enterprise WordPress Security course.
2. Putting a content security policy in place
What kind of content should you allow onto a web page? What should be blocked by default?
These are questions you answer through a content security policy (CSP). Without one, your risk profile skyrockets.
If you’ve never established a CSP before, don’t worry. We’ve put together a set of best practices, including which directives work best for your organization’s needs.
3. Battle-hardening WordPress sites and applications
Complement your training and CSP with a five-point plan to defend against malicious attacks and protect sensitive data.
This not only includes vulnerability management, but also internal network security and how to recover from a breach. There’s a WordPress VIP-specific suggestion for every area covered here.
We asked, you answered
In our last issue of The Brief, we asked you to share the hardest part about being accountable for AI-assisted work. 44% cited explaining AI outputs.
To some, this included being unable to verify that an output was correct. Others admitted they didn’t fully trust the AI software they were using and noted there’s an art to creating prompts that deliver quality responses.
One respondent poetically described this as a uniquely human challenge:
“Keeping the glass clear on AI-driven decisions while firmly holding the human hand on the steering wheel for every outcome.”
If you want help in this area, check out our introduction to AI auditability and explainability.