As Generative Ai (Genai) tools become embedded in the fabric of enterprise operations, they brings transformative promise, but also also considerable risk.
For Cisos, The Challenge Lies in Facilitating Innovation While Securing Data, MainTaINING COMPLIANCE ACROOSS Borders, and Preparing for the Unpredent Nature of Large language models and ai agents.
The stakes are high; A Compromised or Poorly Governed Ai Tool Cold Exposes Sensitive Data, Violate Global Data Laws, or make critical decisions based on false or manipulated inputs.
To mitigate these risks, cisos mustrthink their cyber security strategies and policies Across Three Core Areas: Data Use, Data Sovereignty, and Ai Safety.
Data Use: undersrstanding the terms before sharing vital information
The most pressing risk in AI adoption is not malicious actors but ignorance. Too many organisations integrate Third-party ai tools Without fully understanding how their data will be used, stored, or shared. Most Ai Platforms Are Trained on Vast Swaths of Public Data Scraped from the Internet, often with Little Regard for the source.
While The Larger Players in the Industry, Like Microsoft and Google, Have Started Embedding More Ethical Safguards and Transparency into their terms of service, much of the fine printers and Subject to change.
For cisos, this means rewriting data-shaking policies and procurement checks. Ai tools should be treated as third-party vendors with high-Risk access. Before Deployment, Security Teams Must Audit AI Platform Terms of Use, Assess where and how Enterprise data might be retained or reused, and ensure opt-in power.
Investing in External Consultants or AI Governance Specialists who undress these nuanced contracts can also protect organizations from inadvertent sharing Propriestry Information. In Essence, data used with ai must be treated like a Valuable Export which is carefully consulted, tracked, and registered.
Data Sovereignty: Guardrails for a Borderless Technology
One of the Hidden Dangers in Ai Integration is the Blurring of Geographical Boundaries when it comes to data. What Complies with Data Laws in One Country May Not in another.
For multinationals, this creates a miniefield of potential regulatory breaches, particularly under acts such as Dora and the forthcoming UK Cyber Security and Resilience Bill As well as frameworks like the eu's gdpr or the uk data protection act.
CISOS MUST Adapt their security strategies to ensure ai platforms align with regional data sovereignty requires, which means reviewing where ai systems are hosted, how data flows beetween jurisdictions, And WHETHER APPROPRATE DATA TRANSFER Mechanisms
Where ai tools do not offer adequate locate location or compliance capability, Security Teams must Consider Applying Geofencing, Data Masking, Data Masking, or even local ai deployments.
Policy updates should mandate that Data Localization Preferences Be Enforced for Sensitive or Regulated Datasets, and AI Procurement Processes Should Clear Quases About Cross-Burder Dat Handling. Ultimately, ENSURING DATA Remains Within the Bounds of Compliance is a Legal Isue as Well as a Security Imperative.
Safety: Designing Resilience Into Ai Deployments
The Final Pillar of Ai Security Lies in Safeguarding Systems from the Growing Threat of Manipulation, be it through Prompt Injection AttackS, model hallucinations, or insider misuse.
While Still An Emerging Threat Category, Prompt injection has become one of the most discussed vectors in genai security. By cleverly crafting input strings, attackers can override expected behaviors or extract confidential information from a model. In more extrame examples, AI models have even hallucinated bizarre or harmful outputs, with one system reportedly refusing to be shut down by demolpers.
For CISOS, The Response must be twofold. First, internal controls and red-teaming exercises, like traditional penetration testing, should be adapted to stress-test ai systems. Techniques like chaos engineering can help simulate edge cases and uncover flws before they're exploited.
Second, there needs to be a cultural shift in how vendors are selected. Security Policies Should Favor Ai Providers who Demonstrate Rigorous Testing, Robust Safety Mechanisms, and clear ethical frameworks. Wholes vendors may come at a premium, the potential cost of trusting an unteged ai tool is far green.
To reinforce accountability, Cisost Should also Advocate for Contracts that Place Responsibility on ai vendors for operational failures or unsafe outputs. A Well-Written Agreement should address liability, Incident response procedus, and escalation routes in the event of a malfunction or breach.
From gatekeeper to enabler
As AI BCOMES A Core Part of Business Infrastructure, CISOS MUST Evolve from Being Gatekeepers of Security to Enablers of Safe Innovation. Updating Policies Around Data Use, Strengthaning Controls Over Data Sovereignty, And Building a Layred Safety Net For Ai Deployments will be essential to unlocking the Full Potential of Genai White Compromising Trust, Compliance, or Integrity.
The best defense to the rapid changes caused by ai is proactive, strategic adaptation rooted in knowledge, collaboration, and an unrelacing focus on responsibility.
Elliott wilkes is cto at Advanced Cyber Defense SystemsA Seasoned Digital Transformation Leader and Product Manager, Wilkes Has Over a Decade of Experience Working with BOTH THE BOTH THE BRTH THE BRITISH GOVERNMENTS, Most Recently Service.