AI for Cybersecurity: Building Trust in Your Workflows
Cybersecurity is a high-stakes game where speed matters, but trust is crucial. The ability to respond rapidly and make reliable decisions can mean the difference between a successful attack and a swift defense. However, speed without trust can be just as dangerous – if not more so – than no action at all.
A hasty, inaccurate decision can disrupt critical systems, cause unnecessary downtimes, and erode confidence in your security operations. This is why AI in cybersecurity is about more than just faster detection and response; it's about building trust into every decision the system and analysts make. The gap between knowing something is wrong and doing something about it is one of the most dangerous problems in cybersecurity.
Attackers thrive in this gap, exploiting delays, gaining a more secure foothold, and leaving defenders scrambling to close them down. AI is helping to close that gap by both speeding up response times and making workflows more accurate, reliable, and tailored to the specific needs of relevant organizations.
Trust is the Most Important Cybersecurity Metric
In practice, trust in a security operation comes down to two key standards: operational, measurable requirements. These requirements ensure that even with traditional automation, inaccuracy can cause real damage. A misconfigured playbook for credential stuffing, for example, could lock out hundreds of legitimate users if the detection logic is flawed.
An overzealous phishing prevention workflow could quarantine critical business emails. When the wrong action happens at machine speed, the impact is immediate and widespread. This highlights why trust matters more in the agentic era of AI.
The Agentic Era: More Decision Points, Greater Risk
Agentic AI systems don't just follow rules; they investigate, decide, and act in real time, adapting as situations evolve. This means there are more decision points where accuracy and reliability matter, both in how well the system follows a plan and whether it chooses the right plan in the first place.
For example, an agentic AI system detecting malicious lateral movement in a network might: adjust its actions as new indicators appear, and apply targeted countermeasures that minimize operational disruption. This judgement must be accurate, reliable, and transparent. A false move could cut legitimate administrative sessions, disrupt critical operations, or trigger unnecessary failovers in production systems.
How to Operationalize Trust in AI
When AI systems can act independently, trust stems from having the operational guardrails and feedback loops that make that trust justified. This means defining clear boundaries for autonomous action, validating performance under real-world conditions, and maintaining a continuous feedback loop between human analysts and AI systems.
There are platforms out there that demonstrate how this works in practice. The best platforms adapt to each customer's environment and provide visibility into every decision, making it easy for analysts to validate actions and refine future responses.
About the Author
Josh Breaker-Rolfe is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
Follow me on Twitter: @securityaffairs and Facebook and Mastodon