AI for Cybersecurity: Building Trust in Your Workflows
In cybersecurity, speed matters, but trust is crucial. AI must ensure both rapid response and reliable decisions to avoid errors and disruption.
In cybersecurity, speed matters. But speed without trust can be just as dangerous – if not more so – as no action at all. A hasty, inaccurate decision can disrupt critical systems, cause unnecessary downtimes, and erode confidence in your security operations.
That’s 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 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.
In practice, trust in a security operation comes down to two key standards:
- Accuracy: Does it correctly identify threats and execute the intended action without unnecessary disruption?
- Reliability: Does it do so consistently across different scenarios, environments, and timeframes?
For AI in cybersecurity, these are operational, measurable requirements.
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.
Agentic AI compounds these risks. The first wave of AI mostly just accelerated existing workflows. It still relied on human–defined playbooks, meaning the biggest risk was a bad script executed too quickly.
Agentic AI systems, however, don’t just follow rules. They investigate, decide, and act in real time, adapting as situations evolve.
That means there are more decision points where accuracy and reliability matter, both in how well the system follows a plan, and also in whether it chooses the right plan in the first place.
For example, an agentic AI system detecting malicious lateral movement in a network might:
- Correlate authentication logs from Active Directory, endpoint telemetry from EDR tools, and east-west network traffic patterns to identify suspicious credential use.
- Decide to disable the affected Kerberos tickets and revoke specific OAuth tokens associated with the compromised accounts – rather than locking all users out of the domain.
- Adapt mid-response if it detects new privilege escalation attempts, automatically deploying a just-in-time PAM policy to restrict access to sensitive systems.
- Trigger an IDS/IPS rule update in real time to block further lateral connections from the identified source hosts.
In this scenario, the AI is not running a pre-coded “disable account” script. It is making multi-layered containment decisions based on live telemetry, adjusting its actions as new indicator appear, and applying targeted countermeasures that minimize operational disruption.
That 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.
Ultimately, trust is the permission slip for letting AI operate with this level of autonomy. Without proven accuracy and reliability, you can’t confidently hand over decisions that happen in seconds and impact your business operations.
When AI systems can act independently, trust stems having the operational guardrails and feedback loops that make that trust justified. This means:
- Defining Clear Guardrails: Set the boundaries for what AI can act on autonomously versus what needs human intervention.
- Testing in Real-World Scenarios: Simulate incidents across your environments to validate both accuracy and reliability before deployment.
- Building Continuous Feedback Loops: Feed analyst review and telemetry back into the system to it learn and improves over time.
- Measuring Trust Over Time: Track metrics like true positive rates, mean time to contain (MTTC), and consistency across incident types.
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.
As AI systems take on more autonomous roles in security operations, the margin for error is getting smaller. Accuracy and reliability have become prerequisites for deployment.
Operationalizing trust 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. Only then can analysts trust their workflows.
About the Author: JoshBreaker-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:@securityaffairsandFacebookandMastodon
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