Artificial intelligence is now becoming a common topic in boardrooms, with some leaders viewing it as a tool to boost their company’s security posture and respond quickly to threats, while others focus on its risks, such as deepfakes, AI-driven attacks, and unclear regulations. The key challenge for executives is to move beyond the hype and instead focus on how AI can truly benefit their organization. While AI isn’t a solution for everything, leaders should identify where AI adds real value and ensure it’s used responsibly.
Why This Matters to Leaders
AI has already transitioned from theory to practice in cybersecurity: Security operations teams utilize AI-driven analytics to detect anomalies more quickly. Fraud teams rely on it to spot suspicious transactions. Attackers, however, are also using AI to generate more convincing phishing emails, create deepfake audio to impersonate executives, and automate vulnerability scanning.
Gartner predicts that by 2026, over 40 percent of enterprises will rely on AI-augmented tools to help security teams reduce fatigue and improve response times. IBM’s Cost of a Data Breach Report 2023 found that organizations using AI in security saved an average of $1.76 million per breach compared to those that did not.
For boards and executives, the real question isn’t if AI is present in your environment – it’s already there in the tools you use. The real issue is whether your organization is treating AI as both an opportunity and a risk.
Opportunities and Risks Executives Need to Weigh
Opportunities
- Faster detection and response: AI helps spot and respond to threats more quickly, which can reduce the cost of security breaches.
- Less analyst fatigue: Automation filters out routine alerts, letting analysts spend more time on complex problems.
- Stronger fraud prevention: AI helps catch fraudulent transactions as they happen, making fraud prevention much stronger.
- Operational efficiency: AI-powered tools make it easier to monitor and protect even the largest and most complex systems.
- Proactive threat hunting: AI models spot patterns and unusual activity sooner than traditional tools, giving teams an earlier warning about possible attacks.
- Better user experience: Smarter fraud prevention and flexible authentication keep customers safe without making things harder for them, which helps the business grow.
- Better resource allocation: When routine tasks are automated, teams can spend more time on high-risk issues and important projects.
Risks
- Overreliance: Relying too much on AI can cause organizations to miss threats if the system fails or someone tampers with it.
- Adversary use: Attackers are now using AI to launch more phishing attacks, create deepfakes, and find security weaknesses.
- Compliance and governance gaps: New rules about AI, transparency, and bias are appearing around the world. Weak oversight can lead to fines and hurt a company’s reputation.
- Ethical and accountability concerns: When an AI system makes a mistake, it isn’t always clear who should take responsibility.
- False sense of security: Some leaders may think that having AI means all risks are managed, which can result in less focus on human skills or other important safeguards.
- Data quality issues: AI systems depend on the data they use for training, so poor or biased data can lead to mistakes and gaps in performance.
- Vendor dependency: Using AI features from a single supplier can make it hard to switch providers and may reduce visibility into how decisions are made.
What Executives Should Ask
Practical questions to raise with your teams:
- Where is AI already being used in our security operations, fraud prevention, or IT tools?
- What evidence shows that AI-enabled solutions improve detection, response time, or cost efficiency?
- How are we preparing for AI-enabled threats such as deepfake fraud, automated phishing, or adversarial attacks on AI models?
- Do we have governance in place for AI use, including accountability, testing, and regulatory compliance?
- Are we training our workforce to use AI responsibly, and to challenge AI-generated results when needed?
- Are we engaging legal and compliance teams early as we expand AI adoption?
- What is our risk appetite for AI use in critical decision-making, and is this documented at the board level?
- Do our contracts with AI vendors include transparency requirements, clear security obligations, and audit rights?
- Are we testing AI models for bias, manipulation, or adversarial vulnerabilities?
From a CISO’s Seat
- Start with visibility: Inventory where AI is already present in your systems and tools.
- Demand measurable outcomes: Ensure you have data showing if AI is speeding up response times, cutting down on false alarms, or catching more fraud.
- Establish governance early: Put policies in place that spell out who’s responsible, how you will test, and what compliance steps are needed before you use AI more widely.
- Prepare for AI-driven threats: Update your incident response plans and tabletop exercises to cover deepfakes, AI-powered phishing, and tampered models.
- Invest in human judgment: Teach your teams to question AI results and report anything unusual. AI should help people make decisions, not take over completely.
- Engage beyond IT: Using AI affects legal, compliance, HR, and how customers see your company. Ensure leaders from all these areas are part of the conversation.
Executive Takeaways
- AI is already part of your environment, so consider it both an opportunity and a risk.
- Ask for proof that AI tools actually improve business results, rather than just relying on what vendors claim.
- Ensure your incident response plans include AI-related threats, such as deepfakes and automated phishing.
- Set up clear rules, responsibilities, and compliance checks when using AI in your organization.
- Give your teams the training and confidence to question AI results and make the final call themselves.
- Consider AI as a broader business risk, not just something for the IT department to handle.
The Bottom Line
AI is already making a difference in cybersecurity by shaping both how we defend and face threats. Leaders who treat AI as a governance and risk matter can better identify what really matters; while executives don’t need to know every technical detail, they should ensure that AI supports their business goals, risk limits, and compliance needs.