Quick answer
AI helps defenders detect anomalies, reduce false alarms and respond faster, but attackers use the same technology for convincing phishing, deepfakes and faster exploit development. Businesses should use AI to strengthen monitoring, while testing their own AI and LLM applications for prompt injection, data leakage and access control flaws.
How does AI help cyber defenders?
AI is best at finding patterns in large volumes of data, which is exactly the problem a Security Operations Centre faces every day.
- Detecting unusual logins, data transfers and user behaviour
- Grouping related alerts into a single incident
- Reducing false positives so analysts focus on real threats
- Automating triage and response playbooks
- Prioritising vulnerabilities by real-world exploitability
- Summarising incidents and logs for faster investigation
How are attackers using AI?
The same capabilities are available to criminals, often through cheap or free tools. The table compares attacker use of AI with the defensive response.
| Attacker technique | Impact | Defence |
|---|---|---|
| AI-written phishing | Flawless, personalised emails in any language | MFA, email security, awareness training |
| Voice and video deepfakes | Fake CEO calls approving payments | Call-back verification for payments |
| Automated reconnaissance | Faster discovery of exposed assets | Attack surface monitoring, VAPT |
| Malware assistance | Faster creation of new variants | Behaviour-based EDR |
| Password guessing | Smarter credential attacks | MFA, breached password checks |
What new risks do AI and LLM applications create?
If your product includes a chatbot, AI assistant or LLM-powered feature, it brings risks that traditional testing may miss. The OWASP Top 10 for LLM Applications lists the most important ones.
Prompt injection, where a user or a document tricks the model into ignoring its instructions, can expose data or trigger actions the user should not be allowed to take. Models connected to internal tools or databases need the same access controls as any other user.
| Risk | Example |
|---|---|
| Prompt injection | Hidden instructions in a web page change the bot's behaviour |
| Sensitive data disclosure | Bot reveals other customers' data |
| Excessive agency | AI agent performs actions without approval |
| Insecure output handling | Model output executed as code or HTML |
| Training data leakage | Confidential data appears in responses |
How should employees use AI tools safely?
Staff often paste source code, customer lists or contracts into public AI tools. A short, clear policy prevents accidental data leaks.
- Approve specific AI tools and block unapproved ones
- Never paste personal data, credentials or confidential code
- Prefer enterprise plans that do not train on your data
- Review AI-generated code before using it
- Log and monitor use of AI services
Will AI replace security analysts?
No. AI is excellent at sorting and summarising, but attackers adapt, context matters and decisions such as isolating a production server need human judgement. The most effective SOCs combine AI-driven detection with experienced analysts and threat hunters.
AI does change the skills required. Analysts spend less time on repetitive triage and more on investigation, threat hunting and improving detection rules.
How do regulations treat AI security?
India does not yet have a dedicated AI law, but existing rules apply. Personal data used in AI systems is covered by the DPDP Act, regulated entities must apply RBI and SEBI controls to AI vendors, and incidents involving AI systems still fall under CERT-In reporting requirements. Globally, frameworks such as ISO/IEC 42001 for AI management systems are emerging.
How KCyber Experts helps you use AI securely
KCyber Experts tests AI and LLM applications for prompt injection, data leakage and authorisation flaws as part of our web and API VAPT, and our SOC combines machine learning detection with human analysts for 24x7 protection.
How should businesses start adopting AI for security?
Begin with problems where AI has a proven track record, such as email security, user behaviour analytics and alert triage in the SOC. Measure results against clear metrics like false positive rates and time to detect, rather than relying on vendor claims.
At the same time, create an inventory of where AI is already used in your organisation, including unofficial tools adopted by individual teams. Assess each for data exposure, vendor security and regulatory impact, and assign an owner. This simple governance step prevents most AI-related surprises and builds a foundation for responsible use as AI capabilities grow.
Frequently asked questions
Is AI making cyber attacks more common?
AI lowers the skill and cost needed for attacks like phishing, which increases their volume and quality.
Can you pentest an AI chatbot?
Yes. We test chatbots and LLM features for prompt injection, data exposure and access control flaws alongside standard testing.
What is prompt injection?
Prompt injection is when crafted input makes an AI model ignore its instructions, potentially leaking data or performing unintended actions.
How can we stop deepfake payment fraud?
Require call-back verification on a known number and dual approval for any payment or bank detail change.
Does the DPDP Act apply to AI training data?
Yes, if the training data contains personal data of people in India, DPDP obligations apply.
What is ISO/IEC 42001?
ISO/IEC 42001 is an international standard for AI management systems that helps organisations govern AI responsibly, similar to how ISO 27001 governs information security.
Are AI security tools expensive?
Many AI features are now built into existing email security, EDR and SIEM products, so organisations can often start without buying separate tools.